Tag Archives: Ostrea lurida

Assembly Comparison – Oly Assemblies Using Quast

I ran Quast to compare all of our current Olympia oyster genome assemblies.

See Jupyter Notebook in Results section for Quast execution.

Results:

Output folder: http://owl.fish.washington.edu/Athaliana/quast_results/results_2017_11_14_12_30_25/

Heatmapped table of results: http://owl.fish.washington.edu/Athaliana/quast_results/results_2017_11_14_12_30_25/report.html

Very enlightening!

BEST OF:

Largest Contig: redundans_sjw_02 (322,397bp)
Total Length: soap_bgi_01 & pbjelly_sjw_01 (697,528,655bp)
Total Length (>=50,000bp): redundans_sjw_03 (17,006,058bp)
N50: redundans_sjw_03 (17,679bp)

Interesting tidbit: The pbjelly_sjw_01 assembly is EXACTLY the same as the soap_bgi_01. Looking at the output messages from that PB Jelly assembly, one can see why. The messages indicate that no gaps were filled on the BGI scaffold reference! That means the PB Jelly output is just the BGI scaffold reference assembly!

Jupyter Notebook (GitHub): 20171114_swoose_oly_assembly_comparisons_quast.ipynb

Genome Assembly – Olympia Oyster Illumina & PacBio Using PB Jelly w/BGI Scaffold Assembly

Yesterday, I ran PB Jelly using Sean’s Platanus assembly, but that didn’t produce an assembly because PB Jelly was expecting gaps in the Illumina reference assembly (i.e. scaffolds, not contigs).

Re-ran this using the BGI Illumina scaffolds FASTA.

Here’s a brief rundown of how this was run:

See the Jupyter Notebook for full details of run (see Results section below).

Results:

Output folder: http://owl.fish.washington.edu/Athaliana/20171114_oly_pbjelly/

Output FASTA file: http://owl.fish.washington.edu/Athaliana/20171114_oly_pbjelly/jelly.out.fasta

OK! This seems to have worked (and it was quick, like less than an hour!), as it actually produced a FASTA file! Will run QUAST with this and some assemblies to compare assembly stats. Have added this assembly to our Olympia oyster genome assemblies table.

Jupyter Notebook (GitHub): 20171114_emu_pbjelly_BGI_scaffold.ipynb

Genome Assembly – Olympia Oyster Illumina & PacBio Using PB Jelly w/Platanus Assembly

Sean had previously attempted to run PB Jelly, but ran into some issues running on Hyak, so I decided to try this on Emu.

Here’s a brief rundown of how this was run:

See the Jupyter Notebook for full details of run (see Results section below).

Results:

Output folder: http://owl.fish.washington.edu/Athaliana/20171113_oly_pbjelly/

This completed very quickly (like, just a couple of hours). I also didn’t experience the woes of multimillion temp file production that killed Sean’s attempt at running this on Mox (Hyak).

However, it doesn’t seem to have produced an assembly!

Looking through the output, it seems as though it didn’t produce an assembly because there weren’t any gaps to fill in the reference. This makes sense (in regards to the lack of gaps in the reference Illumina assembly) because I used the Platanus contig FASTA file (i.e. not a scaffolds file). I didn’t realize PB Jelly was just designed for gap filling. Guess I’ll give this another go using the BGI scaffold FASTA file and see what we get.

Jupyter Notebook (GitHub): 20171113_emu_pbjelly_22mer_plat.ipynb

Software Crash – Olympia oyster genome assembly with Masurca on Mox

Ah, the joys of bioinformatics. I just received an email from Mox indicating that the Masurca assembly I started 11 DAYS AGO (!!) crashed.

I’m probably not going to put much effort in to trying to figure out what went wrong, but here’s some log file snippets for reference. I’ll probably drop a line to the developers and see if they have any easy ways to address whatever caused the problems, but that’s about as much effort as I’m willing to put into troubleshooting this assembly.

Additionally, since this crashed, I’m not going to bother moving any of the files off of Mox. That means they will be deleted automatically by the system around Nov. 9th, 2017.


slurm-94620.out (tail)

compute_psa 6601202 2632582819
Refining alignments
Joining
Generating assembly input files
Coverage of the mega-reads less than 5 -- using the super reads as well
Coverage threshold for splitting unitigs is 138 minimum ovl 63
Running assembly
/gscratch/srlab/programs/MaSuRCA-3.2.3/bin/deduplicate_unitigs.sh: line 85: 24330 Aborted                 (core dumped) overlapStoreBuild -o $ASM_DIR/$ASM_PREFIX.ovlStore -M 65536 -g $ASM_DIR/$ASM_PREFIX.gkpStore $ASM_DIR/overlaps_dedup.ovb.gz > $ASM_DIR/overlapStore.rebuild.err 2>&1
Assembly stopped or failed, see CA.mr.41.15.17.0.029.log
[Mon Oct 30 23:19:37 PDT 2017] Assembly stopped or failed, see CA.mr.41.15.17.0.029.log

CA.mr.41.15.17.0.029.log (tail)

number of threads     = 28 (OpenMP default)

ERROR:  overlapStore '/gscratch/scrubbed/samwhite/20171019_masurca_oly_assembly/CA.mr.41.15.17.0.029/genome.ovlStore' is incomplete; previous overlapStoreBuild probably crashed.

----------------------------------------
Failure message:

failed to unitig

overlapStore.rebuild.err

Scanning overlap files to count the number of overlaps.
Found 277.972 million overlaps.
Memory limit 65536MB supplied.  Ill put 3246167525 IIDs (3435.97 million overlaps) into each of 1 buckets.
bucketizing CA.mr.41.15.17.0.029/overlaps_dedup.ovb.gz
bucketizing DONE!
overlaps skipped:
               0 OBT - low quality
               0 DUP - non-duplicate overlap
               0 DUP - different library
               0 DUP - dedup not requested
terminate called after throwing an instance of std::bad_alloc
  what():  std::bad_alloc

Failed with Aborted

Backtrace (mangled):

overlapStoreBuild[0x40523a]
/usr/lib64/libpthread.so.0(+0xf100)[0x2af83b3c0100]
/usr/lib64/libc.so.6(gsignal+0x37)[0x2af83c0395f7]
/usr/lib64/libc.so.6(abort+0x148)[0x2af83c03ace8]
/usr/lib64/libstdc++.so.6(_ZN9__gnu_cxx27__verbose_terminate_handlerEv+0x165)[0x2af83b62d9d5]
/usr/lib64/libstdc++.so.6(+0x5e946)[0x2af83b62b946]
/usr/lib64/libstdc++.so.6(+0x5e973)[0x2af83b62b973]
/usr/lib64/libstdc++.so.6(+0x5eb93)[0x2af83b62bb93]
/usr/lib64/libstdc++.so.6(_Znwm+0x7d)[0x2af83b62c12d]
/usr/lib64/libstdc++.so.6(_Znam+0x9)[0x2af83b62c1c9]
overlapStoreBuild[0x402e10]
/usr/lib64/libc.so.6(__libc_start_main+0xf5)[0x2af83c025b15]
overlapStoreBuild[0x403089]

Backtrace (demangled):

[0] overlapStoreBuild() [0x40523a]
[1] /usr/lib64/libpthread.so.0::(null) + 0xf100  [0x2af83b3c0100]
[2] /usr/lib64/libc.so.6::(null) + 0x37  [0x2af83c0395f7]
[3] /usr/lib64/libc.so.6::(null) + 0x148  [0x2af83c03ace8]
[4] /usr/lib64/libstdc++.so.6::__gnu_cxx::__verbose_terminate_handler() + 0x165  [0x2af83b62d9d5]
[5] /usr/lib64/libstdc++.so.6::(null) + 0x5e946  [0x2af83b62b946]
[6] /usr/lib64/libstdc++.so.6::(null) + 0x5e973  [0x2af83b62b973]
[7] /usr/lib64/libstdc++.so.6::(null) + 0x5eb93  [0x2af83b62bb93]
[8] /usr/lib64/libstdc++.so.6::operator new(unsigned long) + 0x7d  [0x2af83b62c12d]
[9] /usr/lib64/libstdc++.so.6::operator new[](unsigned long) + 0x9  [0x2af83b62c1c9]
[10] overlapStoreBuild() [0x402e10]
[11] /usr/lib64/libc.so.6::(null) + 0xf5  [0x2af83c025b15]
[12] overlapStoreBuild() [0x403089]

GDB:

Genome Assembly – Olympia oyster Illumina & PacBio Reads Using Redundans

Had problems with Docker and Jupyter Notebook inexplicably dying and deleting all the files in the working directory of the Jupyter Notebook (which also happened to be the volume mounted in the Docker container).

So, I ran this on my computer, but didn’t have Jupyter installed (yet).

This utilized the Canu contigs file (FASTA) that I generated on 20171018.

Here’s the input command:

sudo python /home/sam/software/redundans/redundans.py -t 24 -l m130619_081336_42134_c100525122550000001823081109281326_s1_p0.fastq.gz m170211_224036_42134_c101073082550000001823236402101737_s1_X0_filtered_subreads.fastq.gz m170301_100013_42134_c101174162550000001823269408211761_s1_p0_filtered_subreads.fastq.gz m170301_162825_42134_c101174162550000001823269408211762_s1_p0_filtered_subreads.fastq.gz m170301_225711_42134_c101174162550000001823269408211763_s1_p0_filtered_subreads.fastq.gz m170308_163922_42134_c101174252550000001823269408211742_s1_p0_filtered_subreads.fastq.gz m170308_230815_42134_c101174252550000001823269408211743_s1_p0_filtered_subreads.fastq.gz m170315_001112_42134_c101169372550000001823273008151717_s1_p0_filtered_subreads.fastq.gz m170315_063041_42134_c101169382550000001823273008151700_s1_p0_filtered_subreads.fastq.gz m170315_124938_42134_c101169382550000001823273008151701_s1_p0_filtered_subreads.fastq.gz m170315_190851_42134_c101169382550000001823273008151702_s1_p0_filtered_subreads.fastq.gz -i 151114_I191_FCH3Y35BCXX_L1_wHAIPI023992-37_1.fq.gz 151114_I191_FCH3Y35BCXX_L1_wHAIPI023992-37_2.fq.gz 151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_1.fq.gz 151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_2.fq.gz 151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_1.fq.gz 151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_2.fq.gz 160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_1.fq.gz 160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_2.fq.gz 160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_1.fq.gz 160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_2.fq.gz 160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_1.fq.gz 160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_2.fq.gz 160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_1.fq.gz 160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_2.fq.gz 160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_1.fq.gz 160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_2.fq.gz 160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_1.fq.gz 160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_2.fq.gz -f 20171018_oly_pacbio.contigs.fasta -o /home/data/20171024_docker_oly_redundans_01/

This completed in just over 19hrs.

Copied output files to Owl: http://owl.fish.washington.edu/Athaliana/20171024_docker_oly_redundans_01/

Here’s the desired output file (FASTA): scaffolds.reduced.fa

Will add to our genome assemblies table.

Ran Quast on 20171103 for some assembly stats.

Quast output is here: http://owl.fish.washington.edu/Athaliana/quast_results/results_2017_11_03_22_43_06/

Genome Assembly – Olympia oyster Illumina & PacBio reads using MaSuRCA

UPDATE 20171031 – This crashed. No plans to troubleshoot.

Ran this on Mox (hyak) node.

Create single PacBio FASTA file:

list of PacBio files:

m130619_081336_42134_c100525122550000001823081109281326_s1_p0.fastq m170315_001112_42134_c101169372550000001823273008151717_s1_p0_filtered_subreads.fastq.gz
m170211_224036_42134_c101073082550000001823236402101737_s1_X0_filtered_subreads.fastq.gz m170315_063041_42134_c101169382550000001823273008151700_s1_p0_filtered_subreads.fastq.gz
m170301_100013_42134_c101174162550000001823269408211761_s1_p0_filtered_subreads.fastq.gz m170315_124938_42134_c101169382550000001823273008151701_s1_p0_filtered_subreads.fastq.gz
m170301_162825_42134_c101174162550000001823269408211762_s1_p0_filtered_subreads.fastq.gz m170315_190851_42134_c101169382550000001823273008151702_s1_p0_filtered_subreads.fastq.gz
m170301_225711_42134_c101174162550000001823269408211763_s1_p0_filtered_subreads.fastq.gz
m170308_163922_42134_c101174252550000001823269408211742_s1_p0_filtered_subreads.fastq.gz
m170308_230815_42134_c101174252550000001823269408211743_s1_p0_filtered_subreads.fastq.gz

Concatenate GZIPPED files:

$cat *.gz > pacbio_cat.fastq.gz

Convert FASTQ gzip to FASTA:

$zcat pacbio_cat.fastq.gz | awk 'NR%4==1{printf ">%s\n", substr($0,2)}NR%4==2{print}' > oly_pacbio_concatentated.fa

Convert and concatenate single non-gzipped FASTQ file:

$awk 'NR%4==1{printf ">%s\n", substr($0,2)}NR%4==2{print}' m130619_081336_42134_c100525122550000001823081109281326_s1_p0.fastq >> oly_pacbio_concatentated.fa
$cat *.gz > pacbio_cat.fastq.gz

GUNZIP Illumina GZIPPED FASTQ

$for i in *.gz; do gunzip < "$i" > ${i%%.gz}; done

Determine mean read length and standard deviation from Illumina FASTQ files (needed for MaSuRCA config file) (found code below from this Biostars thread:

$for i in *.fq; do echo "$i   "; awk 'BEGIN { t=0.0;sq=0.0; n=0;} ;NR%4==2 {n++;L=length($0);t+=L;sq+=L*L;}END{m=t/n;printf("total %d avg=%f stddev=%f\n",n,m,sq/n-m*m);}' $i; done

Output:

151114_I191_FCH3Y35BCXX_L1_wHAIPI023992-37_1.fq   
total 61253141 avg=150.000000 stddev=0.000000
151114_I191_FCH3Y35BCXX_L1_wHAIPI023992-37_2.fq   
total 61253141 avg=150.000000 stddev=0.000000
151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_1.fq   
total 58755925 avg=150.000000 stddev=0.000000
151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_2.fq   
total 58755925 avg=150.000000 stddev=0.000000
151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_1.fq   
total 43938762 avg=150.000000 stddev=0.000000
151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_2.fq   
total 43938762 avg=150.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_1.fq   
total 87198584 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_2.fq   
total 87198584 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_1.fq   
total 43766527 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_2.fq   
total 43766527 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_1.fq   
total 87135961 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_2.fq   
total 87135961 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_1.fq   
total 43138844 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_2.fq   
total 43138844 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_1.fq   
total 95270180 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_2.fq   
total 95270180 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_1.fq   
total 92524416 avg=49.000000 stddev=0.000000
160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_2.fq   
total 92524416 avg=49.000000 stddev=0.000000

Here’s the config file I’m using:

/gscratch/scrubbed/samwhite/20171019_masurca_oly_assembly/20171019_masurca_oly_config.txt

# example configuration file

# DATA is specified as type {PE,JUMP,OTHER,PACBIO} and 5 fields:
# 1)two_letter_prefix 2)mean 3)stdev 4)fastq(.gz)_fwd_reads
# 5)fastq(.gz)_rev_reads. The PE reads are always assumed to be
# innies, i.e. --->.<---, and JUMP are assumed to be outties
# <---.--->. If there are any jump libraries that are innies, such as
# longjump, specify them as JUMP and specify NEGATIVE mean. Reverse reads
# are optional for PE libraries and mandatory for JUMP libraries. Any
# OTHER sequence data (454, Sanger, Ion torrent, etc) must be first
# converted into Celera Assembler compatible .frg files (see
# http://wgs-assembler.sourceforge.com)
DATA
PE= pe 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_2.fq
PE= pe 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_2.fq
PE= pe 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_2.fq
PE= pe 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_2.fq
PE= pe 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_2.fq
PE= pe 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_2.fq

JUMP= sh 150 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151114_I191_FCH3Y35BCXX_L1_wHAIPI023992-37_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151114_I191_FCH3Y35BCXX_L1_wHAIPI023992-37_2.fq
JUMP= sh 150 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_2.fq
JUMP= sh 150 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_2.fq
#pacbio reads must be in a single fasta file! make sure you provide absolute path
PACBIO=/gscratch/scrubbed/samwhite/O_lurida/oly_pacbio/oly_pacbio_concatentated.fa
END

PARAMETERS
#this is k-mer size for deBruijn graph values between 25 and 127 are supported, auto will compute the optimal size based on the read data and GC content
GRAPH_KMER_SIZE = auto
#set this to 1 for all Illumina-only assemblies
#set this to 1 if you have less than 20x long reads (454, Sanger, Pacbio) and less than 50x CLONE coverage by Illumina, Sanger or 454 mate pairs
#otherwise keep at 0
USE_LINKING_MATES = 1
#this parameter is useful if you have too many Illumina jumping library mates. Typically set it to 60 for bacteria and 300 for the other organisms
LIMIT_JUMP_COVERAGE = 300
#these are the additional parameters to Celera Assembler.  do not worry about performance, number or processors or batch sizes -- these are computed automatically.
#set cgwErrorRate=0.25 for bacteria and 0.1<=cgwErrorRate<=0.15 for other organisms.
CA_PARAMETERS =  cgwErrorRate=0.15
#minimum count k-mers used in error correction 1 means all k-mers are used.  one can increase to 2 if Illumina coverage >100
KMER_COUNT_THRESHOLD = 1
#whether to attempt to close gaps in scaffolds with Illumina data
CLOSE_GAPS=1
#auto-detected number of cpus to use
NUM_THREADS = 28
#this is mandatory jellyfish hash size -- a safe value is estimated_genome_size*estimated_coverage
JF_SIZE = 200000000
#set this to 1 to use SOAPdenovo contigging/scaffolding module.  Assembly will be worse but will run faster. Useful for very large (>5Gbp) genomes
SOAP_ASSEMBLY=0
END

Execute the masurca script to generate assembly script (on Mox login node):

$cd /gscratch/scrubbed/samwhite/20171019_masurca_oly_assembly
$/gscratch/srlab/programs/MaSuRCA-3.2.3/bin/masurca 20171019_masurca_oly_config.txt

Got this error:

I’ll edit config file to have standard deviations == 1 and try again.

Got another error:

I’ll edit config file to have different two letter prefix assignments…

It worked!

Final version of config:

# example configuration file

# DATA is specified as type {PE,JUMP,OTHER,PACBIO} and 5 fields:
# 1)two_letter_prefix 2)mean 3)stdev 4)fastq(.gz)_fwd_reads
# 5)fastq(.gz)_rev_reads. The PE reads are always assumed to be
# innies, i.e. --->.<---, and JUMP are assumed to be outties
# <---.--->. If there are any jump libraries that are innies, such as
# longjump, specify them as JUMP and specify NEGATIVE mean. Reverse reads
# are optional for PE libraries and mandatory for JUMP libraries. Any
# OTHER sequence data (454, Sanger, Ion torrent, etc) must be first
# converted into Celera Assembler compatible .frg files (see
# http://wgs-assembler.sourceforge.com)
DATA
PE= aa 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_2.fq
PE= ab 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_2.fq
PE= ac 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_2.fq
PE= ad 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_2.fq
PE= ae 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_2.fq
PE= af 49 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_2.fq

JUMP= ba 150 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151114_I191_FCH3Y35BCXX_L1_wHAIPI023992-37_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151114_I191_FCH3Y35BCXX_L1_wHAIPI023992-37_2.fq
JUMP= bb 150 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_2.fq
JUMP= bc 150 1 /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_1.fq /gscratch/scrubbed/samwhite/O_lurida/oly_illumina/151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_2.fq
#pacbio reads must be in a single fasta file! make sure you provide absolute path
PACBIO=/gscratch/scrubbed/samwhite/O_lurida/oly_pacbio/oly_pacbio_concatentated.fa
END

PARAMETERS
#this is k-mer size for deBruijn graph values between 25 and 127 are supported, auto will compute the optimal size based on the read data and GC content
GRAPH_KMER_SIZE = auto
#set this to 1 for all Illumina-only assemblies
#set this to 1 if you have less than 20x long reads (454, Sanger, Pacbio) and less than 50x CLONE coverage by Illumina, Sanger or 454 mate pairs
#otherwise keep at 0
USE_LINKING_MATES = 1
#this parameter is useful if you have too many Illumina jumping library mates. Typically set it to 60 for bacteria and 300 for the other organisms
LIMIT_JUMP_COVERAGE = 300
#these are the additional parameters to Celera Assembler.  do not worry about performance, number or processors or batch sizes -- these are computed automatically.
#set cgwErrorRate=0.25 for bacteria and 0.1<=cgwErrorRate<=0.15 for other organisms.
CA_PARAMETERS =  cgwErrorRate=0.15
#minimum count k-mers used in error correction 1 means all k-mers are used.  one can increase to 2 if Illumina coverage >100
KMER_COUNT_THRESHOLD = 1
#whether to attempt to close gaps in scaffolds with Illumina data
CLOSE_GAPS=1
#auto-detected number of cpus to use
NUM_THREADS = 28
#this is mandatory jellyfish hash size -- a safe value is estimated_genome_size*estimated_coverage
JF_SIZE = 200000000
#set this to 1 to use SOAPdenovo contigging/scaffolding module.  Assembly will be worse but will run faster. Useful for very large (>5Gbp) genomes
SOAP_ASSEMBLY=0
END

Submitted the job to Mox using the following command:

sbatch -p srlab -A srlab 20171019_masurca_oly_assembly.sh

Here’s the sbatch script used for the job:

#!/bin/bash
## Job Name
#SBATCH --job-name=20171019_masurca_oly
## Allocation Definition
#SBATCH --account=srlab
#SBATCH --partition=srlab
## Resources
## Nodes (We only get 1, so this is fixed)
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=30-00:00:00
## Memory per node
#SBATCH --mem=500G
##turn on e-mail notification
#SBATCH --mail-type=ALL
#SBATCH --mail-user=
## Specify the working directory for this job
#SBATCH --workdir=/gscratch/scrubbed/samwhite/20171019_masurca_oly_assembly
/gscratch/scrubbed/samwhite/20171019_masurca_oly_assembly/assemble.sh

Genome Assembly – Olympia oyster PacBio Canu v1.6

I decided to run Canu myself, since documentation for Sean’s Canu run is a bit lacking. Additionally, it looks like he specified a genome size of 500Mbp, which is probably too small. For this assembly, I set the genome size to 1.9Gbp (based on the info in the BGI assembly report, using 17-mers for calculating genome size), which is probably on the large size.

Additionally, I remembered we had an old PacBio run that we had been forgetting about and thought it would be nice to have incorporated into an assembly.

See all the messy details of this in the Jupyter Notebook below, but here’s the core info about this Canu assembly.

PacBio Input files (available on Owl/nightingales/O_lurida:

m170308_163922_42134_c101174252550000001823269408211742_s1_p0_filtered_subreads.fastq.gz                                                               m170308_230815_42134_c101174252550000001823269408211743_s1_p0_filtered_subreads.fastq.gz
m130619_081336_42134_c100525122550000001823081109281326_s1_p0.fastq                       m170315_001112_42134_c101169372550000001823273008151717_s1_p0_filtered_subreads.fastq.gz
m170211_224036_42134_c101073082550000001823236402101737_s1_X0_filtered_subreads.fastq.gz  m170315_063041_42134_c101169382550000001823273008151700_s1_p0_filtered_subreads.fastq.gz
m170301_100013_42134_c101174162550000001823269408211761_s1_p0_filtered_subreads.fastq.gz  m170315_124938_42134_c101169382550000001823273008151701_s1_p0_filtered_subreads.fastq.gz
m170301_162825_42134_c101174162550000001823269408211762_s1_p0_filtered_subreads.fastq.gz  m170315_190851_42134_c101169382550000001823273008151702_s1_p0_filtered_subreads.fastq.gz
m170301_225711_42134_c101174162550000001823269408211763_s1_p0_filtered_subreads.fastq.gz

Canu execution command (see the Jupyter Notebook below for more info):

$time canu \
useGrid=false \
-p 20171009_oly_pacbio \
-d /home/data/20171018_oly_pacbio_canu/ \
genomeSize=1.9g \
correctedErrorRate=0.075 \
-pacbio-raw m*

Results:

Well, this took a LONG time to run; a bit over two days!

The report file contains some interesting tidbits. For instance:

  • Unitgigging calculates only 1.84x coverage
  • Trimming removed >5 billion (!!) bases: 867850 reads 5755379456 bases (reads with no overlaps, deleted)
  • Unitigging unassembled: unassembled: 479693 sequences, total length 2277137864 bp

I’ll compare this Canu assembly against Sean’s Canu assembly and see how things look.

Report file (text file): http://owl.fish.washington.edu/Athaliana/20171018_oly_pacbio_canu/20171018_oly_pacbio.report

Contigs Assembly (FASTA): http://owl.fish.washington.edu/Athaliana/20171018_oly_pacbio_canu/20171018_oly_pacbio.contigs.fasta

Complete Canu output directory: http://owl.fish.washington.edu/Athaliana/20171018_oly_pacbio_canu/

Jupyter Notebook (GitHub): 20171018_docker_oly_canu.ipynb

Data Management – Convert Oly PacBio H5 to FASTQ

After working with all of this Olympia oyster genome sequencing data, I remembered that we had an old, singular PacBio SMRT cell file (from June 2013). This file didn’t seem to be included in any recent assemblies of Sean’s or mine. This is most likely because we have it in the PacBio H5 format and not in FASTQ.

I installed PacBio’s pbh5tools on my computer (swoose), converted the file and moved it to owl/nightingales/O_lurida

python bash5tools.py /mnt/owl/nightingales/O_lurida/m130619_081336_42134_c100525122550000001823081109281326_s1_p0.bas.h5 --outType fastq 

I generated an MD5 checksum and appended to the checksums.md5 file in /owl/nightingales/O_lurida using the following command:

md5sum m130619_081336_42134_c100525122550000001823081109281326_s1_p0.fastq | awk '{print $2 " = " $1}' >> checksums.md5

The command above pipes the output to awk to format the output to match the existing format of the checksums.md5 file (i.e. filename = hash).

I’ve also updated our Nightingales spreadsheet (Google Sheet) to reflect this.

Will generate updated PacBio assemblies with Canu and/or Racon.

Genome Assembly – Olympia oyster Redundans/Canu vs. Redundans/Racon

Decided to compare the Redundans using Canu as reference and Redundans using Racon as reference. Both reference assemblies were just our PacBio data.

Jupyter notebook (GitHub): 20171005_docker_oly_redundans.ipynb

Notebook is also embedded at the end of this post.

Results:

It should be noted that the paired reads for each of the BGI mate-pair Illumina data did not assemble, just like last time I used them:

  • 160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCABDLAAPEI-62_2.fq.gz
  • 160103_I137_FCH3V5YBBXX_L3_WHOSTibkDCACDTAAPEI-75_2.fq.gz
  • 160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCABDLAAPEI-62_2.fq.gz
  • 160103_I137_FCH3V5YBBXX_L4_WHOSTibkDCACDTAAPEI-75_2.fq.gz
  • 160103_I137_FCH3V5YBBXX_L5_WHOSTibkDCAADWAAPEI-74_2.fq.gz
  • 160103_I137_FCH3V5YBBXX_L6_WHOSTibkDCAADWAAPEI-74_2.fq.gz

Redundans with Canu is better, suggesting that the Canu assembly is the better of the two PacBio assemblies (which we had already suspected).

QUAST comparison using default settings:

Interactive link:http://owl.fish.washington.edu/Athaliana/quast_results/results_2017_10_06_22_21_06/report.html

QUAST comparison using –scaffolds setting:

Interactive link: http://owl.fish.washington.edu/Athaliana/quast_results/results_2017_10_06_22_27_26/report.html

Assembly Comparisons – Olympia oyster genome assemblies

— UPDATE 20171009 —

Having run through this a bunch of times now, I realized that the analysis below incorrectly identifies the outputs from Sean’s Redundans runs. The correct output from each of those runs should be the “scaffolds.reduced.fa” FAST files. The “contigs.fa” files that I linked to below are actually the assemblies produced by other programs; which are required as an input for Redudans.


I recently completed an assembly of the UW PacBio sequencing data using Racon and wanted some assembly stats, as well as a way to compare this assembly to the assemblies Sean had completed.

Additionally, Steven recently performed an assembly comparison and I noticed he got some odd results. Specifically, of the three assemblies he compared (PacBio x 1, Illumina x 2), both of the Illumina assemblies had a large quantity of “Ns” in the assemblies. This didn’t seem right and the comparison program he used (QUAST) spit out a message indicating that it seemed like scaffolds were used, instead of contigs. So, I thought I’d give it a shot and see if I could track down non-scaffolded assemblies produced by Sean.

Jupyter notebook (GitHub): 20171003_docker_oly_assembly_comparisons.ipynb

First, I compared the following six assemblies (FASTA files) using QUAST:

Sean’s Assemblies:

Sam’s Assembly:

QUAST output directory: http://owl.fish.washington.edu/Athaliana/20171003_quast_oly_genome_assemblies/

Here’s the assembly comparison of all assemblies (click on image for larger view):

Interactive version of that graphic is here: http://owl.fish.washington.edu/Athaliana/20171003_quast_oly_genome_assemblies/report.html

The first thing that jumps out to me is the fact that two of the Illumina assemblies, which used different assemblers(!!) have the EXACT same assembly stats. This occurrence seems extremely unlikely. I’ve double-checked my Jupyter notebook to make sure that I didn’t assign the same file by accident (see Input #6)

Very strange!

I also noticed that the first Redundans assembly of Sean’s has a ton of “Ns”, suggesting that it’s actually a scaffolded assembly. As with Steven’s QUAST run, QUAST spits out the messages suggesting to use the “–scaffold” option for this file.

The other thing I noticed is the two PacBio assemblies (Canu & Racon) have a huge difference in the total number of bp (~13,000,000)! I ran a QUAST assembly comparison between just those two for easier viewing/comparison (http://owl.fish.washington.edu/Athaliana/20171003_quast_oly_pacbio_assemblies/):

Interactive version of that graphic is here: http://owl.fish.washington.edu/Athaliana/20171003_quast_oly_pacbio_assemblies/report.html

The fact that there is such a large discrepancy in the total number of bps between these two assemblies really leaves me to believe that I am missing a FASTQ file from my assembly. I’m going to go back and see if that is indeed the case or if this difference in the assemblies is real.

Here’s an embedded version of my Jupyter notebook: