Iterative error correction of long 250 or 300 bp Illumina reads minimizes the total amount of erroneous reads, which improves contig assembly. This pipeline runs multiple rounds of k-mer-based correction with an increasing k-mer size, followed by a final round of overlap-based correction. By combining the advantages of small and large k-mers, this pipeline is able to correct more base substitution errors, especially in repeats. The final overlap-based correction round can also correct small insertions and deletions.

References:

• Sameith K, Roscito J, Hiller M (2016). Iterative error correction of long sequencing reads maximizes accuracy and improves contig assembly. Briefings in Bioinformatics, doi: 10.1093/bib/bbw003

• SGA-ICE Main Site

• Module Name: SGA-ICE (see the modules page for more information)
• Multithreaded app (use -t option)
• Example files in /usr/local/apps/SGA-ICE/TEST_DATA

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive -c 8 --mem=20g 
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load SGA-ICE

[user@cn3144 ~]$ cd /data/$USER

[user@cn3144 ~]$ cp -r /usr/local/apps/SGA-ICE/TEST_DATA .

[user@cn3144 ~]$ SGA-ICE.py /data/$USER/TEST_DATA -k 40,60,100,125,150,200 -t 8 --noCleanup --noOvlCorr --scriptName test_sga.sh
### Input directory is: TEST_DATA/
### Number of threads is: 8
### User provided k-mers are: [40, 60, 100, 125, 150, 200]
### User chose not to do overlap correction
### Temporary files will not be deleted
### Error rate for sga correct 0.01
### Minimum overlap for sga correct 40
Shell script is called: test_sga.sh
### These are the fastq files found in /data/teacher/TEST_DATA/:
SRR6219624_1.fastq
SRR6219624_2.fastq
### Your reads are 301 bp long, calculated from file: /data/teacher/TEST_DATA/SRR6219624_1.fastq

To run 6 correction round with k=40,60,100,125,150,200 using 8 threads, execute 
/data/teacher/TEST_DATA//test_sga.sh

[user@cn3144 ~]$ cd TEST_DATA

[user@cn3144 ~]$ ./test_sga.sh
Temporary directory is: /tmp/tmp.K1DyIAaV1g
### Start sga preprocessing ###
preprocess: WARNING - it is suggested that the min read length is 40
preprocess: Using very short reads may considerably impact the performance
Parameters:
QualTrim: 0
QualFilter: no filtering
HardClip: 0
Min length: 0
Sample freq: 1
PE Mode: 0
Quality scaling: 2
MinGC: 0
MaxGC: 1
Outfile: SRR6219624_1.SGAICEpreproc.fastq
Orphan file: none
Seed: 1527619454
Processing SRR6219624_1.fastq
[...]

[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Create a batch input file (e.g. SGA-ICE.sh). For example:

#!/bin/bash
module load SGA-ICE

cd /data/teacher
cp -r /usr/local/apps/SGA-ICE/TEST_DATA .
SGA-ICE.py TEST_DATA -k 40,60,100,125,150,200 -t $SLURM_CPUS_PER_TASK --noCleanup --noOvlCorr --scriptName test_sga.sh
cd TEST_DATA
./test_sga.sh

Submit this job using the Slurm sbatch command.

sbatch --cpus-per-task=8 --mem=20g SGA-ICE.sh

Create a swarmfile (e.g. SGA-ICE.swarm). For example:

cd /dir/fastq/1; SGA-ICE.py /dir/fastq/1 -t $SLURM_CPUS_PER_TASK [...] run1.sh; ./run1.sh 
cd /dir/fastq/2; SGA-ICE.py /dir/fastq/2 -t $SLURM_CPUS_PER_TASK [...] run2.sh; ./run2.sh
cd /dir/fastq/3; SGA-ICE.py /dir/fastq/3 -t $SLURM_CPUS_PER_TASK [...] run3.sh; ./run3.sh

Submit this job using the swarm command.

swarm -f SGA-ICE.swarm -g 10 -t 8 --module SGA-ICE