Category Archives: Miscellaneous

RNA Isolation – Tanner Crab Hemolymph Pellet in RNAlater using TriReagent

I previously isolated RNA from crab hemolymp from a lyophilized sample using TriReagent and Grace recently tried isolating RNA from crab hemolyph pellet (non-lyophilized) using TriReagent. The results for her extractions weren’t so great, so I’m giving it a shot with the following samples:

  • crab 424

  • crab 429

  • crab 438

Isolated RNA using TriReagent, according to manufacturer’s protocol:

Added 1mL TriReagent to each tube, vortexed to mix/dissolve solute, incubated 5mins at RT, added 200uL of chloroform, vortexed 15s to mix, incubated at RT for 5mins, centrifuged 15mins, 12,000g, 4oC, transferred aqueous phase to new tube, added 500uL isopropanol to aqueous phase, mixed, incubated at RT for 10mins, centrifuged 8mins, 12,000g, at RT, discarded supernatant, added 1mL 75% ethanol, centrifuged 5mins, 12,000g at RT, discarded supernatant and resuspended in 10uL of 0.1% DEPC-treated H2O.

Phase separation after chloroform addition was not particularly good. Aqueous phases in sample 424 was a bit cloudy (salty?) with no defined interphase. The remaining two samples did exhibit a defined interphase and were the aqueous phases were less cloudy than sample 424, but were far from ideal.

Quantified RNA using Roberts Lab Qubit 3.0 with the Qubit RNA high sensitivity kit. Used 5uL of each sample.

RESULTS

No detectable RNA in any samples. Samples were discarded.

As has been the case for all samples in this project, RNA isolation methodologies have produced wildly inconsistent results.

RNA Isolation – Ronit’s C.gigas diploid/triploid dessication/heat shock ctenidia tissues

Isolated RNA from a subset of Ronit’s Crassostrea gigas ctenidia samples (see Ronit’s notebook for experiment deets):

  • D01 C

  • D02 C

  • D19 C

  • D20 C

  • T01 C

  • T02 C

  • T19 C

  • T20 C

RNA was isolated using RNAzol RT (Molecular Research Center) in the following way:

  • Unweighed, frozen tissue transferred to 500uL of RNAzol RT and immediately homogenized with disposable pestle.

  • Added additional 500uL of RNAzol RT and vortexed to mix.

  • Added 400uL of 0.1% DEPC-treated H2O, vortexed and incubated 15mins at RT.

  • Centrifuged 12,000g for 15mins at RT.

  • Transferred 750uL of supernatant to clean tube (discarded remainder), added 1 volume (750uL) of isopropanol, vortexed, and incubated at RT for 10mins.

  • Centrifuged 12,000g for 10mins at RT.

  • Discarded supernatant.

  • Washed pellet with 75% ethanol (made with 0.1% DEPC-treated H2O).

  • Centrifuged 4,000g for 2mins at RT.

  • Discarded supernatant and repeated wash steps.

Pellet was resuspended in 50uL of 0.1% DEPC-treated H2O and stored @ -80oC in Ronit’s temporary box.

Installation – Microsoft Machine Learning Server (Microsoft R Open) on Emu/Roadrunner R Studio Server

Steven recently saw an announcement that Microsoft R Open now handles multi-threaded processing (default R does not), so we were interested in trying it out. I installed MLR/MRO on Emu/Roadrunner (Apple Xserve; Ubuntu 16.04). Followed the Microsoft installation directions for Ubuntu. In retrospect, I think I could’ve just installed MRO, but this gets the job done as well and won’t hurt anything.

I’ve set both Emu & Roadrunner R Studio Server to use this installation of R by changing the /etc/restudio/rserver.conf file to the following:


# Server Configuration File

# Use Microsoft R Open instead of default R version.
# Comment out and restart R Studio Server (sudo rstudio-server restart)
# to restore default R version.

rsession-which-r=/opt/microsoft/ropen/3.4.3/lib64/R/bin/R

I have confirmed that R Studio Server on both machines starts up and is using MRO instead of the default version of R.

Transcriptome Alignment & Bedgraph – Olympia oyster transcriptome with Olurida_v080 genome assembly

Yesterday, I produced a bedgraph file of our Olympia oyster RNAseq data coverage using our Olurida_v081 genome.

I decided that I wanted to use the Olurida_v080 version instead (or, in addtion to?), as the Olurida_v080 version has not been size restricted (the Olurida v081 version is only contigs >1000bp). I feel like we could miss some important regions, so wanted to run this analysis using all of the genome data we currently have available. Additionally, this will be consistent with my previous Bismark (DNA methylation analysis).

Used HISAT2 on our HPC Mox node to align our RNAseq reads to our Olurida_v080 genome assembly:

SBATCH script file:

NOTE: For brevity sake, I have not listed all of the input RNAseq files below. Please see the full script, which is linked above.


#!/bin/bash
## Job Name
#SBATCH --job-name=20180926_oly_hisat2
## Allocation Definition 
#SBATCH --account=srlab
#SBATCH --partition=srlab
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=5-00:00:00
## Memory per node
#SBATCH --mem=500G
##turn on e-mail notification
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samwhite@uw.edu
## Specify the working directory for this job
#SBATCH --workdir=/gscratch/scrubbed/samwhite/20180926_oly_RNAseq_genome_hisat2_bedgraph

# Load Python Mox module for Python module availability

module load intel-python3_2017

# Document programs in PATH (primarily for program version ID)

date >> system_path.log
echo "" >> system_path.log
echo "System PATH for $SLURM_JOB_ID" >> system_path.log
echo "" >> system_path.log
printf "%0.s-" {1..10} >> system_path.log
echo ${PATH} | tr : \\n >> system_path.log


# Set genome assembly path
oly_genome_path=/gscratch/srlab/sam/data/O_lurida/oly_genome_assemblies

# Set sorted transcriptome assembly bam file
oly_transcriptome_bam=20180926_Olurida_v080.sorted.bam

# Set hisat2 basename
hisat2_basename=Olurida_v080

# Set program paths
## hisat2
hisat2=/gscratch/srlab/programs/hisat2-2.1.0

## bedtools
bedtools=/gscratch/srlab/programs/bedtools-2.27.1/bin

## samtools
stools=/gscratch/srlab/programs/samtools-1.9/samtools

# Build hisat2 genome index
${hisat2}/hisat2-build \
-f ${oly_genome_path}/Olurida_v080.fa \
Olurida_v080 \
-p 28

# Align reads to oly genome assembly
${hisat2}/hisat2 \
--threads 28 \
-x "${hisat2_basename}" \
-q \
-1 \
-2 \
-S 20180926_"${hisat2_basename}".sam

# Convert SAM file to BAM
"${stools}" view \
--threads 28 \
-b 20180926_"${hisat2_basename}".sam > 20180926_"${hisat2_basename}".bam

# Sort BAM
"${stools}" sort \
--threads 28 \
20180926_"${hisat2_basename}".bam \
-o 20180926_"${hisat2_basename}".sorted.bam

# Index for use in IGV
##-@ specifies thread count; --thread option not available in samtools index
"${stools}" index \
-@ 28 \
20180926_"${hisat2_basename}".sorted.bam


# Create bedgraph
## Reports depth at each position (-bg in bedgraph format) and report regions with zero coverage (-a).
## Screens for portions of reads coming from exons (-split).
## Add genome browser track line to header of bedgraph file.
${bedtools}/genomeCoverageBed \
-ibam ${oly_transcriptome_bam} \
-bga \
-split \
-trackline \
> 20180926_oly_RNAseq.bedgraph

The script performs the following functions:

  • Genome indexing
  • RNAseq alignment to genome
  • Convert SAM to BAM
  • Sort and index BAM
  • Determine RNAseq coverage

RESULTS

Output folder:

Bedgraph file (1.9GB):

Loaded in to IGV to verify things looked OK:

Bedgraph – Olympia oyster transcriptome with Olurida_v081 genome assembly

I took the sorted BAM file from yesterday’s corrected RNAseq genome alignment and converted it to a bedgraph using BEDTools genomeCoverageBed tool.

Analysis took place on our HPC Mox node.

SBATCH script file:


#!/bin/bash
## Job Name
#SBATCH --job-name=20180926_oly_bedgraphs
## Allocation Definition 
#SBATCH --account=srlab
#SBATCH --partition=srlab
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=5-00:00:00
## Memory per node
#SBATCH --mem=500G
##turn on e-mail notification
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samwhite@uw.edu
## Specify the working directory for this job
#SBATCH --workdir=/gscratch/scrubbed/samwhite/20180926_oly_RNAseq_bedgraphs

# Load Python Mox module for Python module availability

module load intel-python3_2017

# Document programs in PATH (primarily for program version ID)

date >> system_path.log
echo "" >> system_path.log
echo "System PATH for $SLURM_JOB_ID" >> system_path.log
echo "" >> system_path.log
printf "%0.s-" {1..10} >> system_path.log
echo ${PATH} | tr : \\n >> system_path.log

# Set sorted transcriptome assembly bam file
oly_transcriptome_bam=/gscratch/scrubbed/samwhite/20180925_oly_RNAseq_genome_hisat2/20180925_Olurida_v081.sorted.bam


# Set program paths
bedtools=/gscratch/srlab/programs/bedtools-2.27.1/bin
samtools=/gscratch/srlab/programs/samtools-1.9/samtools


# Create bedgraph
## Reports depth at each position (-bg in bedgraph format) and report regions with zero coverage (-a).
## Screens for portions of reads coming from exons (-split).
## Add genome browser track line to header of bedgraph file.
${bedtools}/genomeCoverageBed \
-ibam ${oly_transcriptome_bam} \
-bga \
-split \
-trackline \
> 20180926_oly_RNAseq.bedgraph

RESULTS

Output folder:

Bedgraph file (1.2GB):

Loaded in to IGV to verify things looked OK:

Transcriptome Alignment – Olympia oyster RNAseq reads aligned to genome with HISAT2

Yesterday’s attempt at producing a bedgraph was a failure and a prodcuct of a major brain fart. The worst part is that I was questioning what I was doing the entire time, but still went through with the process! Yeesh!

The problem was that I tried to take our Trinity-assembled transcriptome and somehow align that to our genome. This can’t work because each of those assemblies don’t know the coordinates used by the other. So, as was the case, you end up with a bedgraph that shows zero coverage for all genome contigs.

Anyway, here’s the correct procedure!

Used HISAT2 on our HPC Mox node to align our RNAseq reads to our Olurida_v081 genome assembly:

SBATCH script files:

PERFORM GENOME INDEXING & ALIGNMENT
– 20180925_oly_RNAseq_genome_hisat2.sh

SORT & INDEX ALIGNMENT OUTPUT
– 20180925_oly_RNAseq_genome_sort_bam.sh

RESULTS

Output folder:

Sorted BAM file (58GB):

Will get the sorted BAM file converted to a bedgraph showing genome coverage for use in IGV.

Bedgraph – Olympia oyster transcriptome (FAIL)

Progress on generating bedgraphs from our Olympia oyster transcriptome continues.

Transcriptome assembly with Trinity completed 20180919.

Then, aligned the assembled transcriptome to our genome using Bowtie2.

Finally, I used BEDTools to convert the BAM to BED to bedgraph.

This required an initial indexing of our Olympia oyster genome FastA using samtools faidx tool.

SBATCH script file:


#!/bin/bash
## Job Name
#SBATCH --job-name=20180924_oly_bedgraphs
## Allocation Definition 
#SBATCH --account=srlab
#SBATCH --partition=srlab
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=5-00:00:00
## Memory per node
#SBATCH --mem=500G
##turn on e-mail notification
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samwhite@uw.edu
## Specify the working directory for this job
#SBATCH --workdir=/gscratch/scrubbed/samwhite/20180924_oly_RNAseq_bedgraphs

# Load Python Mox module for Python module availability

module load intel-python3_2017

# Document programs in PATH (primarily for program version ID)

date >> system_path.log
echo "" >> system_path.log
echo "System PATH for $SLURM_JOB_ID" >> system_path.log
echo "" >> system_path.log
printf "%0.s-" {1..10} >> system_path.log
echo ${PATH} | tr : \\n >> system_path.log


# Set genome assembly FastA
oly_genome_fasta=/gscratch/srlab/sam/data/O_lurida/oly_genome_assemblies/Olurida_v081.fa

# Set indexed genome assembly file
oly_genome_indexed=/gscratch/srlab/sam/data/O_lurida/oly_genome_assemblies/Olurida_v081.fa.fai

# Set sorted transcriptome assembly bam file
oly_transcriptome=/gscratch/scrubbed/samwhite/20180919_oly_transcriptome_bowtie2/20180919_Olurida_v081.sorted.bam


# Set program paths
bedtools=/gscratch/srlab/programs/bedtools-2.27.1/bin
samtools=/gscratch/srlab/programs/samtools-1.9/samtools

# Index genome FastA
${samtools} faidx ${oly_genome_fasta}

# Format indexed genome for bedtools
## Requires only two columns: namelength
awk -v OFS='\t' {'print $1,$2'} ${oly_genome_indexed} > Olurida_v081.fa.fai.genome

# Create bed file
${bedtools}/bamToBed \
-i ${oly_transcriptome} \
> 20180924_oly_RNAseq.bam.bed


# Create bedgraph
## Reports depth at each position (-bg in bedgraph format) and report regions with zero coverage (-a).
## Screens for portions of reads coming from exons (-split).
## Add genome browser track line to header of bedgraph file.
${bedtools}/genomeCoverageBed \
-i ${PWD}/20180924_oly_RNAseq.bed \
-g Olurida_v081.fa.fai.genome \
-bga \
-split \
-trackline \
> 20180924_oly_RNAseq.bed

Alignment was done using the following version of the Olympia oyster genome assembly:

RESULTS:

Output folder:

Indexed and formatted genome file:

Bedgraph file (for IGV):

This doesn’t appear to have worked properly. Here’s a view of the bedgraph file:


track type=bedGraph
Contig0 0   116746  0
Contig1 0   87411   0
Contig2 0   139250  0
Contig3 0   141657  0
Contig4 0   95692   0
Contig5 0   130522  0
Contig6 0   94893   0
Contig7 0   109667  0
Contig8 0   95943   0

I’d expect multiple entries for each contig (ideally), indicating start/stop positions for where transcripts align within a given contig. However, this appears to simply be a list of all the genome contigs and their lengths (Start=0, Stop=n).

I would expect to see something li

I’ll look into this further and see where this pipeline goes wrong.

Transcriptome Alignment – Olympia oyster Trinity transcriptome aligned to genome with Bowtie2

Progress on generating bedgraphs from our Olympia oyster transcriptome continues.

Transcriptome assembly with Trinity completed 20180919.

Next up, align transcriptome to Olympia oyster genome.

Alignment and creation of BAM files was done using Bowtie2 on our HPC Mox node.

SBATCH script file:

Alignment was done using the following version of the Olympia oyster genome assembly:

RESULTS:

Output folder:

Sorted BAM file:

Sorted & indexed BAM file (for IGV):

Will get the sorted BAM file converted to a bedgraph for use in IGV.

Transcriptome Assembly – Olympia oyster RNAseq Data with Trinity

Used all of our current oly RNAseq data to assemble a transcriptome using Trinity.

Trinity was run our our Mox HPC node.

Reads were trimmed using the built-in version of Trimmomatic with the default settings.

SBATCH script:

Despite the naming conventions, this job was submitted to the Mox scheduler on 201800912 and finished on 20180913.

After job completion, the entire folder was gzipped, using an interactive node (the following method of gzipping is SUPER fast, btw):

tar -c 20180827_trinity_oly_RNAseq | pigz > 20180827_trinity_oly_RNAseq.tar.gz

RESULTS:

Output folder:

Trinity assembly (FastA):

Next up, I’ll follow up on this GitHub issue and get some bedgraphs generated.

DNA Methylation Analysis – Olympia Oyster Whole Genome BSseq Bismark Pipeline MethylKit Comparison

I previously ran two variations on the Bismark analysis for our Olympia oyster whole genome bisulfite sequencing data:

I followed this up using the MethylKit R package to identify differentially modified loci (DML), based on differing amounts of coverage (1x, 3x, 5x, & 10x) and percent methylation differences between the two groups of oysters (25%, 50%, & 75%).

See the project wiki for experimental design info).

Both sets of analyses were documented in R Projects:

Default Bismark settings:
“Relaxed” Bismark settings
RESULTS

BedGraphs (1x coverage, 25% diff in methylation):

Default Bismark settings:
“Relaxed” Bismark settings:

The BedGraph outputs from the least stringent coverage/percent difference in methylation for both Bismark pipelines yield suprisingly low numbers of DML.

They yield 22 and 21 DML, respectively. Of course, more stringent BedGraphs have fewer DML, but may be more believable due to having a more robust set of data.

Interestingly, the two analyses reveal that a single contig contains the majority of DML, all within a 1000bp range.

Will continue to examine this data by examining Bismark BedGraphs in IGV, and running some additional MethylKit analysis looking at differentially modified regions(DMRs) to see what we can gleen from this.