Tag Archives: Panopea generosa

Library Construction – Geoduck Water Filter Metagenome with Nextera DNA Flex Kit (Illumina)

Made Illumina libraries with goeduck metagenome water filter DNA I previously isolated on:

We used a free Nextera DNA Flex Kit (Illumina) that we won in a contest held by Illumina!

Followed the manufacturer’s protocol for input DNA quantities <10ng with the following changes/notes:

  • PCR steps performed in 200uL thin-walled PCR tubes.

  • Magnetic separations were performed in 1.7mL snap cap tubes.

  • Thermalcycler: PTC-200 (MJ Research)

  • Magnet: DynaMag 2 (Invitrogen)

See the Library Calcs sheet (link below) for original sample names and subsequent library sample names.

IMPORTANT!

The sheet also contains the indexes used for each library. This info will be necessary for sequencing facility.

Library Calcs (Google Sheet):

Links to the Illumina manuals are below:

After library construction was completed, individual libraries were quantified on the Roberts Lab Qubit 3.0 (Invitrogen) with the Qubit 1x dsDNA HS Assay Kit.

2uL of each sample was used for each assay.

Library quality was assessed using the Seeb Lab 2100 Bioanalyzer (Agilent) with a High Sensitivity DNA Kit, using 1uL of each sample.

Libraries were stored in the small -20C in FTR213:


Results:

Qubit Raw Data (Google Sheet):

Bioanalyzer File (XAD):

All libraries have DNA in them, so that’s good!

Except for one library (Library Geoduck MG #04 is bad), the other libraries look OK (i.e. not great). Compared to the example on Pg. 12 in the manual, these libraries all have some extra high molecular weight stuff.

When selecting the range listed in the Nextera Kit manual, the average fragment size is ~530bp – the expected size should be ~600bp.

Spoke with Steven about Library Geoduck MG #04 and we’ve opted to just leave it out.

All other samples were pooled into a single samples according to the manufacturer’s protocol.

This pooled sample was stored in the same -20C box as above, in position I4.

TrimGalore/FastQC/MultiQC – TrimGalore! RRBS Geoduck BS-seq FASTQ data (directional)

Earlier this week, I ran TrimGalore!, but set the trimming, incorrectly – due to a copy/paste mistake, as --non-directional, so I re-ran with the correct settings.

Steven requested that I trim the Geoduck RRBS libraries that we have, in preparation to run them through Bismark.

These libraries were originally created by Hollie Putnam using the TruSeq DNA Methylation Kit (Illumina):

All analysis is documented in a Jupyter Notebook; see link below.

Overview of process:

  1. Run TrimGalore! with --paired and --rrbs settings.

  2. Run FastQC and MultiQC on trimmed files.

  3. Copy all data to owl (see Results below for link).

  4. Confirm data integrity via MD5 checksums.

Jupyter Notebook:


Results:
TrimGalore! output folder:
FastQC output folder:
MultiQC output folder:
MultiQC report (HTML):

FastQC – RRBS Geoduck BS-seq FASTQ data

Earlier today I finished trimming Hollie’s RRBS BS-seq FastQ data.

However, the original files were never analyzed with FastQC, so I ran it on the original files.

These libraries were originally created by Hollie Putnam using the TruSeq DNA Methylation Kit (Illumina):

FastQC was run, followed by MultiQC. Analysis was run on Roadrunner.

All analysis is documented in a Jupyter Notebook; see link below.

Jupyter Notebook:

Results:
FastQC output folder:
MultiQC output folder:
MultiQC report (HTML):

TrimGalore/FastQC/MultiQC – TrimGalore! RRBS Geoduck BS-seq FASTQ data


20180516 – UPDATE!!

THIS WAS RUN WITH THE INCORRECT SETTING IN TRIMGALORE! --non-directional

WILL RE-RUN


Steven requested that I trim the Geoduck RRBS libraries that we have, in preparation to run them through Bismark.

These libraries were originally created by Hollie Putnam using the TruSeq DNA Methylation Kit (Illumina):

All analysis is documented in a Jupyter Notebook; see link below.

Overview of process:

  1. Copy EPI* FastQ files from owl/P_generosa to roadrunner.

  2. Confirm data integrity via MD5 checksums.

  3. Run TrimGalore! with --paired, --rrbs, and --non-directional settings.

  4. Run FastQC and MultiQC on trimmed files.

  5. Copy all data to owl (see Results below for link).

  6. Confirm data integrity via MD5 checksums.

Jupyter Notebook:


Results:
TrimGalore! output folder:
FastQC output folder:
MultiQC output folder:
MultiQC report (HTML):

Data Management – Illumina NovaSeq Geoduck Genome Sequencing

As part of the Illumina collaborative geoduck genome sequencing project, their end goal has always been to sequence the genome in a single run.

They’ve finally attempted this by running 10x Genomics, Hi-C, Nextera, and TruSeq libraries in a single run of the NovaSeq.

I downloaded the data using the BaseSpace downloader using Chrome on a Windows 7 computer (this is not available on Ubuntu and the command line tools that are available from Illumina are too confusing for me to bother spending the time on to figure out how to use them just to download the data).

Data was saved here:

Generated MD5 checksums (using md5sum on Ubuntu) and appended to the checksums file:

Illumina was unable to provide MD5 checksums on their end, so I was unable to confirm data integrity post-download.

Illumina sample info is here:

Will add info to:

List of files received:

10x-Genomics-Libraries-Geo10x5-A3-MultipleA_S10_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleA_S10_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleA_S10_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleA_S10_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleB_S11_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleB_S11_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleB_S11_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleB_S11_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleC_S12_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleC_S12_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleC_S12_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleC_S12_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleD_S13_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleD_S13_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleD_S13_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleD_S13_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleA_S14_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleA_S14_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleA_S14_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleA_S14_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleB_S15_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleB_S15_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleB_S15_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleB_S15_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleC_S16_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleC_S16_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleC_S16_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleC_S16_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleD_S17_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleD_S17_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleD_S17_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleD_S17_L002_R2_001.fastq.gz
HiC-Libraries-GeoHiC-C3-N701_S18_L001_R1_001.fastq.gz
HiC-Libraries-GeoHiC-C3-N701_S18_L001_R2_001.fastq.gz
HiC-Libraries-GeoHiC-C3-N701_S18_L002_R1_001.fastq.gz
HiC-Libraries-GeoHiC-C3-N701_S18_L002_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP10-B2-AD013_S7_L001_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP10-B2-AD013_S7_L001_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP10-B2-AD013_S7_L002_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP10-B2-AD013_S7_L002_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP11-C2-AD014_S8_L001_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP11-C2-AD014_S8_L001_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP11-C2-AD014_S8_L002_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP11-C2-AD014_S8_L002_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP12-D2-AD015_S9_L001_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP12-D2-AD015_S9_L001_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP12-D2-AD015_S9_L002_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP12-D2-AD015_S9_L002_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP9-A2-AD002_S6_L001_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP9-A2-AD002_S6_L001_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP9-A2-AD002_S6_L002_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP9-A2-AD002_S6_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA1-A1-NR006_S1_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA1-A1-NR006_S1_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA1-A1-NR006_S1_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA1-A1-NR006_S1_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA3-C1-NR012_S2_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA3-C1-NR012_S2_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA3-C1-NR012_S2_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA3-C1-NR012_S2_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA5-E1-NR005_S3_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA5-E1-NR005_S3_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA5-E1-NR005_S3_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA5-E1-NR005_S3_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA7-G1-NR019_S4_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA7-G1-NR019_S4_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA7-G1-NR019_S4_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA7-G1-NR019_S4_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA8-H1-NR021_S5_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA8-H1-NR021_S5_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA8-H1-NR021_S5_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA8-H1-NR021_S5_L002_R2_001.fastq.gz

Assembly – Geoduck Hi-C Assembly Subsetting

Steven asked me to create a couple of subsets of our Phase Genomics Hi-C geoduck genome assembly (pga_02):

  • Contigs >10kbp

  • Contigs >30kbp

I used pyfaidx on Roadrunner and the following commands:

faidx --size-range 10000,100000000 PGA_assembly.fasta > PGA_assembly_10k_plus.fasta
faidx --size-range 30000,100000000 PGA_assembly.fasta > PGA_assembly_30k_plus.fasta

Ran Quast afterwards to get stats on the new FastA files just to confirm that the upper cutoff value was correct and didn’t get rid of the largest contig(s).

Results:

faidx Output folder: 20180512_geoduck_fasta_subsets/

10kbp contigs (FastA): 20180512_geoduck_fasta_subsets/PGA_assembly_10k_plus.fasta

30kbp contigs (FastA): 20180512_geoduck_fasta_subsets/PGA_assembly_30k_plus.fasta

Quast output folder: results_2018_05_14_06_26_26/

Quast report (HTML): results_2018_05_14_06_26_26/report.html

Everything looks good. The main thing I wanted to confirm by running Quast was that the largest contig in each subset was the same as the original PGA assembly (95,480,635bp.

Read Mapping – Mapping Illumina Data to Geoduck Genome Assemblies with Bowtie2

We have an upcoming meeting with Illumina to discuss how the geoduck genome project is coming along and to decide how we want to proceed.

So, we wanted to get a quick idea of how well our geoduck assemblies are by performing some quick alignments using Bowtie2.

Used the following assemblies as references:

  • sn_ph_01 : SuperNova assembly of 10x Genomics data

  • sparse_03 : SparseAssembler assembly of BGI and Illumina project data

  • pga_02 : Hi-C assembly of Phase Genomics data

The analysis is documented in a Jupyter Notebook.

Jupyter Notebook (GitHub):

NOTE: Due to large amount of stdout from first genome index command, the notebook does not render well on GitHub. I recommend downloading and opening notebook on a locally install version of Jupyter.

Here’s a brief overview of the process:

  1. Generate Bowtie2 indexes for each of the genome assemblies.
  2. Map 1,000,000 reads from the following Illumina NovaSeq FastQ files:

Results:

Bowtie2 Genome Indexes:

Bowtie2 sn_ph_01 alignment folder:

Bowtie2 sparse_03 alignment folder:

Bowtie2 pga_02 alignment folder:


MAPPING SUMMARY TABLE

All mapping data was pulled from the respective *.err file in the Bowtie2 alignment folders.

sequence_ID Assembler Alignment Rate (%)
sn_ph_01 SuperNova (10x) 79.89
sparse_03 SparseAssembler 85.83
pga_02 Hi-C (Phase Genomics) 79.90|

Mapping efficiency is similar for all assemblies. After speaking with Steven, we’ve decided we’ll begin exploring genome annotation pipelines.

Assembly & Stats – SparseAssembler (k95) on Geoduck Sequence Data > Quast for Stats

Had a successful assembly with SparseAssembler k101, but figured I’d just tweak the kmer setting and throw it in the queue and see how it compares; minimal effort/time needed.

Initiatied an assembly run using SparseAssembler on our Mox HPC node on all of our geoduck genomic sequencing data:

Kmer size set to 95.

Slurm script: 20180423_sparse_assembler_kmer95_geoduck_slurm.sh

After the run finished, I copied the files to our server (Owl) and then ran Quast on my computer to gather some assembly stats, using the following command:


/home/sam/software/quast-4.5/quast.py \
-t 24 \
--labels 20180423_sparse_k95 \
/mnt/owl/Athaliana/20180423_sparseassembler_kmer95_geoduck/Contigs.txt \

Results:

SparseAssembler output folder: 20180423_sparseassembler_kmer95_geoduck/

SparseAsembler assembley (FastA; 15GB): 20180423_sparseassembler_kmer95_geoduck/Contigs.txt

Quast output folder: quast_results/results_2018_05_10_15_04_07

Quast report (HTML): quast_results/results_2018_05_10_15_04_07/report.html

I’ve embedded the Quast HTML report below, but it may be easier to view by using the link above.

Well, it’s remarkable how different this is than the previous SparseAssembler with k101 setting!

This assembly doesn’t have a single contig >50,000bp, while the previous one has four contigs over that threshold!

Definitely shows what a large impact the kmer setting in assembly software can have on the final assembly!

Assembly Stats – Geoduck Genome Assembly Comparisons w/Quast – SparseAssembler, SuperNova, Hi-C

Steven requested a comparison of geoduck genome assemblies.

Ran the following Quast command:

/home/sam/software/quast-4.5/quast.py \
-t 24 \
--labels 20180405_sparse_kmer101,supernova_pseudohap_duck4-p,20180421_Hi-C \
/mnt/owl/Athaliana/20180405_sparseassembler_kmer101_geoduck/Contigs.txt \
/mnt/owl//halfshell/bu-mox/analyses/0305b/duck4-p.fasta.gz \
/mnt/owl/Athaliana/20180419_geoduck_hi-c/Results/geoduck_roberts\ results\ 2018-04-21\ 18\:09\:04.514704/PGA_assembly.fasta
Results:

Quast output folder: results_2018_04_30_08_00_42/

Quast report (HTML): results_2018_04_30_08_00_42/report.html

The data’s pretty interesting and cool!

SparseAssembler has over 2x the amount of data (in bas pairs), yet produces the worst assembly.

SuperNova and Hi-C assemblies are very close in nearly all categories. This isn’t surprising, as the SuperNova assembly was used as a reference assembly for the Hi-C assembly.

However, the Hi-C assembly is insanely better than the SuperNova assembly! For example:

  • Largest contig is ~7x larger than the SuperNova assembly.
  • The N50 size is ~243x larger than the SuperNova assembly!!
  • L50 is only 18, 46x smaller than the SuperNova assembly!

This is pretty amazing, honestly. Even more amazing is that this data was sent over to us as some “preliminary” data for us to take a peak at!

Assembly Stats – Geoduck Hi-C Assembly Comparison

Ran the following Quast command to compare the two geoduck assemblies provided to us by Phase Genomics:

/home/sam/software/quast-4.5/quast.py \
-t 24 \
--labels 20180403_pga,20180421_pga \
/mnt/owl/Athaliana/20180421_geoduck_hi-c/Results/geoduck_roberts\ results\ 2018-04-03\ 11\:05\:41.596285/PGA_assembly.fasta \
/mnt/owl/Athaliana/20180421_geoduck_hi-c/Results/geoduck_roberts\ results\ 2018-04-21\ 18\:09\:04.514704/PGA_assembly.fasta
Results:

Quast Output folder: results_2018_04_30_11_16_04/

Quast report (HTML): results_2018_04_30_11_16_04/report.html

The two assemblies are nearly identical. Interesting…