# Data Management – SRA Submission Oly GBS Batch Submission

An earlier attempt at submitting these files failed.

I re-uploaded the failed files (indicated in my previous notebook entry linked above) and tried again.

It failed again, despite having successfully uploaded just minutes before.

I re-uploaded that “missing” file and tried again.

This time, it succeeded (and no end-of-stream error for the 1SN_1A file!)!

Will post here with the SRA accession number once it goes live!

# Computing – Oly BGI GBS Reproducibility; fail?

OK, so things have improved since the last attempt at getting this BGI script to run and demultiplex the raw data.

I played around with the index.lst file format (based on the error I received last time, it seemed like a good possibility that the file formatting was incorrect) and actually got the script to run to completion! Granted, it took over 16hrs (!!), but it completed!

See the Jupyter notebook link below.

Results:

Well, although the script finished and kicked out all the demultiplexed FASTQ files, the contents of the FASTQ files don’t match (the read counts differ between these results and the BGI files) the original set of demultiplexed files. I’m not entirely sure if this is to be expected or not, since the script allows for a single nucleotide mismatch when demultiplexing. Is it possible that the mismatch could be interpreted slightly differently each time this is run? I’m not certain.

Theoretically, you should get the same results every time…

Maybe I’ll re-run this again over the weekend and see how the results compare to this run and the original BGI demultiplexing…

Jupyter notebook (GitHub): 20170314_docker_Oly_BGI_GBS_demultiplexing_reproducibility.ipynb

Jupyter notebook (may be easier to view in GitHub link above):

# Computing – Oly BGI GBS Reproducibility Fail (but, less so than last time)…

Well, my previous attempt at reproducing the demultiplexing that BGI performed was an exercise in futility. BGI got back to me with the following message:

Hi Sam,

We downloaded it and it seems fine when compiling. You can compile it with the below command under Linux system.

tar -zxvf ReSeqTools_XXX.tar.gz ; cd iTools_Code; chmod 775 iTools ; ./ iTools -h

I gave that whirl and got the following message:

Error opening terminal: xterm

Some internet searching got me sucked into a useless black hole about 64 bit systems running 32 bit programs and enabling the 64 bit kernel on Mac OS X 10.7.5 (Lion) since it’s not enabled by default and on and on. In the end, I can’t seem to enable the 64 bit kernel on my Mac Pro, likely due to hardware limitations related to the graphics card and/or displays that are connected.

Anyway, I decided to try getting this program installed again, using a Docker container (instead of trying to install locally on my Mac).

Results:

It didn’t work again, but for a different reason! Despite the instructions in the readme file provided with iTools, you don’t actually need to run make! All that has to be done is unzipping the tarball!! However, despite figuring this out, the program fails with the following error message: “Warming : sample double in this INDEX Files. Sample ID: OYSzenG1AAD96FAAPEI-109; please renamed it diff” (note: this is copied/pasted – the spelling errors are note mine). So, I think there’s something wrong with the formatting of the index file that BGI provided me with.

See the Jupyter notebook linked below to see what I tried.

Jupyter notebook (GitHub): 20170314_docker_Oly_BGI_GBS_demultiplexing_reproducibility.ipynb

# Computing – Oly BGI GBS Reproducibility Fail

Since we’re preparing a manuscript that relies on BGI’s manipulation/handling of the genotype-by-sequencing data, I attempted to could reproduce the demultiplexing steps that BGI used in order to perform the SNP/genotyping on these samples.

The key word in the above sentence is “attempted.” Ugh, what a massive waste of time it turned out to be. I’ve contacted BGI to get some help on this.

In the meantime, here’s a brief (actually, not as brief as I’d like) rundown of my struggles.

The demultiplexing software that BGI used is something called “iTools” which is bundled in this GitHub repo: Resqtools

To demutliplex, they ran a script called: split.sh

The script seems fairly straightforward. Here is what it contains:

iTools Fqtools splitpool
-Index index.lst
-Flag enzyme.txt
-MisMatch
-OutDir split


It tells the iTools program to use the Fqtools tool “splitpool” to operate on a pair of gzipped FASTQ files. It also utilizes an index file (index.lst) which contains all the barcodes needed to identify, and separate, the individual samples that were combined prior to sequencing.

The first bump in the road is the -Flag enzyme.txt portion of the code. BGI did not provide me with this file. I recently requested them to send me it (or its contents, since I suspected it was only a single line text file). They sent me the contents of the file:

CAGC
CTGC

The next problem is neither of those two sequences are the recognition site for the enzyme that was (supposedly) used: ApeKI. The recognition site for ApeKI is: GCWGC

Regardless, I decided to see if I could reproduce the demultiplexing using the info they’d provided me.

I cloned the Resqtools repo, changed into the Reseqtools/iTools directory and typed make.

This resulted in an error informing me that it could not find boost/spirit/core.hpp

Tried to run make in the Reseqtools/iTools directory and got the same error. Realized iTools might not be searching the system \$PATH (this turned out to be correct), so I moved the contents of the Boost folder to the iTools, ran make and got the same error. Turns out, the newest version of Boost doesn’t have that core.hpp file any more. Looking at the iTools documentation, iTools was built around Boost 1.44. OMG…

Downloaded Boost 1.44 and went through the same steps as above. This eliminated the missing core.hpp error!

But, of course, led to another error. The error:

"Threading support unavaliable: it has been explicitly disabled with BOOST_DISABLE_THREADS"

That was related to something with newer versions of the GCC compiler (this is, essentially, built into the computer; it’s not worth trying to install/use old versions of GCC) trying to work with old versions of Boost. Found a patch for a config file here: libstdcpp3.hpp.patch

I made the appropriate edits to the file as shown in that link and ran make and it almost worked!

The current error is:

./src/Variants/soapsv-v1.02/include.h:15:16: fatal error: gd.h: No such file or directory

I gave up and contacted BGI to see if they can get me a functional version of iTools…

# FASTQC – Oly BGI GBS Raw Illumina Data Demultiplexed

Last week, I ran the two raw FASTQ files through FastQC. As expected, FastQC detected “errors”. These errors are due to the presence of adapter sequences, barcodes, and the use of a restriction enzyme (ApeKI) in library preparation. In summary, it’s not surprising that FastQC was not please with the data because it’s expecting a “standard” library prep that’s already been trimmed and demultiplexed.

However, just for comparison, I ran the demultiplexed files through FastQC. The Jupyter notebook is linked (GitHub) and embedded below. I recommend viewing the Jupyter notebook on GitHub for easier viewing.

Results:

Pretty much the same, but with slight improvements due to removal of adapter and barcode sequences. The restriction site still leads to FastQC to report errors, which is expected.

Links to all of the FastQC output files are linked at the bottom of the notebook.

Jupyter notebook (GitHub): 20170306_docker_fastqc_demultiplexed_bgi_oly_gbs.ipynb

# FASTQC – Oly BGI GBS Raw Illumina Data

In getting things prepared for the manuscript we’re writing about the Olympia oyster genotype-by-sequencing data from BGI, I felt we needed to provide a FastQC analysis of the raw data (since these two files are what we submitted to the NCBI short read archive) to provide support for the Technical Validation section of the manuscript.

Below, is the Jupyter notebook I used to run the FastQC analysis on the two files. I’ve embedded for quick viewing, but it might be easier to view the notebook via the GitHub link.

Results:

Well, I realized that running FastQC on the raw data might not reveal anything all too helpful. The reason for this is that the adaptor and barcode sequences are still present on all the reads. This will lead to over-representation of these sequences in all of the samples, which, in turn, will skew FastQC’s intepretation of the read qualities. For comparison, I’ll run FastQC on the demultiplexed data provided by BGI and see what the FastQC report looks like on trimmed data.

However, I’ll need to discuss with Steven about whether or not providing the FastQC analysis is worthwhile as part of the “technical validation” aspect of the manuscript. I guess it can’t hurt to provide it, but I’m not entirely sure that the FastQC report provides any real information regarding the quality of the sequencing reads that we received…

Jupyter notebook (GitHub): 20170301_docker_fastqc_nondemultiplexed_bgi_oly_gbs.ipynb

# Data Management – Integrity Check of Final BGI Olympia Oyster & Geoduck Data

After completing the downloads of these files from BGI, I needed to verify that the downloaded copies matched the originals. Below is a Jupyter Notebook detailing how I verified file integrity via MD5 checksums. It also highlights the importance of doing this check when working with large sequencing files (or, just large files in general), as a few of them had mis-matching MD5 checksums!

Although the notebook is embedded below, it might be easier viewing via the notebook link (hosted on GitHub).

At the end of the day, I had to re-download some files, but all the MD5 checksums match and these data are ready for analysis:

Final Ostrea lurida genome files

Final Panopea generosa genome files

Jupyter Notebook: 20161214_docker_BGI_data_integrity_check.ipynb

We received info to download the final data and genome assembly files for geoduck and Olympia oyster from BGI.

In total, the downloads took a little over three days to complete!

The notebook detailing how the files were downloaded is below, but it should be noted that I had to strip the output cells because the output from the download command made the file too large to upload to GitHub, and the size of the notebook file would constantly crash the browser/computer that it was opened in. So, the notebook below is here for posterity.

# Data Management – Tracking O.lurida FASTQ File Corruption

UPDATE 20170104 – These two corrupt files have been replaced with non-corrupt files.

Sean identified an issue with one of the original FASTQ files provided to use by BGI. Additionally, Steven had (unknowingly) identified the same corrupt file, as well as a second corrupt file in the set of FASTQ files. The issue is discussed here: https://github.com/sr320/LabDocs/issues/334

Steven noticed the two files when he ran the program FASTQC and two files generated no output (but no error message!).

The two files in question are:

• 151118_I137_FCH3KNJBBXX_L5_wHAXPI023905-96_1.fq.gz
• 151114_I191_FCH3Y35BCXX_L2_wHAMPI023991-66_2.fq.gz

This post is an attempt to document where things went wrong, but having glanced through this data a bit already, it won’t provide any answers.

I originally downloaded the data on 20160127 to my home folder on Owl (this is detailed in the Jupyter notebook in that post) and generated/compared MD5 checksum values. The values matched at that time.

So, let’s investigate a bit further…

Launch Docker container

docker run - p 8888:8888 -v /Users/sam/data/:/data -v /Users/sam/owl_home/:/owl_home -v /Users/sam/owl_web/:owl_web -v /Users/sam/gitrepos/LabDocs/jupyter_nbs/sam/:/jupyter_nbs -it 0ba43904567e

The command allows access to Jupyter Notebook over port 8888 and makes my Jupyter Notebook GitHub repo and my data files accessible to the Docker container.

Once the container was started, started Jupyter Notebook with the following command inside the Docker container:

jupyter notebook

This command is configured in the Docker container to launch a Jupyter Notebook without a browser on port 8888.

Jupyter notebook file: 20161117_docker_oly_genome_fastq_corruption.ipynb

I’ve embedded the notebook below, but it’s much easier to view (there are many lengthy commands/filenames that wrap lines in the embedded version below) the actual file linked above.

# Data Management – Geoduck Small Insert Library Genome Assembly from BGI

Received another set of Panopea generosa genome assembly data from BGI back in May! I neglected to create MD5 checksums, as well as a readme file for this data set! Of course, I needed some of the info that the readme file should’ve had and it wasn’t there. So, here’s the skinny…

It’s data assembled from the small insert libraries they created for this project.

All data is stored here: http://owl.fish.washington.edu/P_generosa_genome_assemblies_BGI/20160512/

They’ve provided a Genome Survey (PDF) that has some info about the data they’ve assembled. In it, is the estimated genome size:

Geoduck genome size: 2972.9 Mb

Additionally, there’s a table breaking down the N50 distributions of scaffold and contig sizes.

Data management stuff was performed in a Jupyter (iPython) notebook; see below.