Tag Archives: olympia oyster

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:

Genome Assembly – Olympia oyster PacBio minimap/miniasm/racon

In this GitHub Issue, Steven had suggested I try out the minimap/miniasm/racon pipeline for assembling our Olympia oyster PacBio data.

I followed the pipeline described by this paper: http://matzlab.weebly.com/uploads/7/6/2/2/76229469/racon.pdf.

Previously, ran the first part of the pipeline: minimap

This notebook entry just contains the miniasm execution. Will follow with racon.

Jupyter Notebook (GitHub): 20170918_docker_pacbio_oly_miniasm0.2.ipynb

Genome Assembly – Olympia oyster PacBio minimap/miniasm/racon

In this GitHub Issue, Steven had suggested I try out the minimap/miniasm/racon pipeline for assembling our Olympia oyster PacBio data.

I followed the pipeline described by this paper: http://matzlab.weebly.com/uploads/7/6/2/2/76229469/racon.pdf.

This notebook entry just contains the initial minimap execution. Followed up with miniasm and then racon.

Jupyter Notebook (GitHub): 20170907_docker_pacbio_oly_minimap2.ipynb

Project Progress – Olympia Oyster Genome Assemblies by Sean Bennett

Here’s a brief overview of what Sean has done with the Oly genome assembly front.

Metassembler

  • Assemble his BGI assembly and Platanus assembly? Confusing terms here; not sure what he means.
  • Failed due to 32-bit vs. 64-bit installation of MUMmer. He didn’t have the chance to re-compile MUMmer as 64-bit. However, a recent MUMmer announcement suggests that MUMmer can now handle genomes of unlimited size.
  • I believe he was planning on using (or was using?) GARM, which relies upon MUMmer and may also include a version of MUMmer (outdated version that led to Sean’s error message?).
  • Notebook entry

Canu

Redundans

Platanus

Sample Submission – Olympia oyster gonad RNA to Katherine Silliman @ Univ. of Chicago

Sent the following RNA to Katherine Silliman at the Univ. of Chicago for RNAseq. All RNA was isolated on 20170719, except SN-10-16, which was isolated on 20170710.

NF-10-22
NF-10-23
NF-10-24
NF-10-26
NF-10-28
NF-10-30
SN-10-16
SN-10-17
SN-10-20
SN-10-25
SN-10-26
SN-10-31

All samples were sent on dry ice via FedEx Standard Overnight: 779698651635

RNA Isolation – Olympia oyster gonad tissue in paraffin histology blocks

My previous go at this was a little premature – I didn’t wait for Laura to fully annotate her slides/blocks. Little did I know, the tissue was mostly visceral mass and, as such, I didn’t hit much in the way of actual gonad tissue. So, I’m redoing this, now that Grace has gone through and annotated the blocks to point out gonad tissue. SN-10-16 was sent to Katherine Silliman on 20170720.

Isolated RNA from Olympia oyster gonad previously preserved with the PAXgene Tissue Fixative and Stabilizer and then embedded in paraffin blocks. See Laura’s notebook for full details on samples and preservation.

 

RNA was isolated from the following samples using the PAXgene Tissue RNA Kit (Qiagen). Gouged samples from the blocks weighing ~10mg from each of the tissues and processed according the protocol for isolating RNA from blocks of paraffin-embedded tissues.

Background on all of this is in this GitHub Issue

NF-10-22
NF-10-23
NF-10-24
NF-10-26
NF-10-28
NF-10-30
SN-10-16
SN-10-17
SN-10-20
SN-10-25
SN-10-26
SN-10-31

IMPORTANT:

  • Prior to beginning, I prepared an aliquot of Buffer TR1 by adding 40μL of β-mercaptoethanol (β-ME) to 4000μL of Buffer TR1)

Isolated RNA according to the PAXgene Tissue RNA Kit protocol with the following alterations:

  • “Max speed” spins were performed at 20,000g.
  • Tissue disruption was performed by adding ~25-50 glass beads (425 – 600μm diameter) with the Disruptor Genie @ 45C for 15mins (in the Friedman Lab).
  • Shaking incubation step was performed with Disruptor Genie
  • Samples were eluted with 27μL of Buffer TR4 x 2, incubated @ 65C for 5mins, immediately placed on ice.

 

Results:

Samples were not quantified due to lack of proper RNA Qubit assay AND the computer that our NanoDrop1000 is hooked up to is dead. Will have Katherine Silliman perform quantification.

Samples were stored at -80C temporarily.

Samples will be sent to Katherine Silliman for high-throughput library construction and sequencing once I hear back from her regarding her availability to receive the samples.