Tag Archives: jupyter notebook

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

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Genome Assembly – Olympia Oyster Redundans with Illumina + PacBio

Redundans should assemble both Illumina and PacBio data, so let’s do that.

Sean had previously performed this – twice actually:

It wasn’t entirely clear how he had run Redundans the first time and the second time he used his Platinus contig FASTA file as the necessary reference assembly when running Redundans.

Since he had produced a good looking assembly from PacBio data using Canu, I decided to give Redundans a rip using that assembly.

I then compared all three Redundans runs using QUAST.

Jupyter notebook (GitHub): 20171004_docker_oly_redundans.ipynb

Notebook is also embedded at the bottom of this notebook entry (but, it should be easier to view at the link provided above).

Of note, is that Redundans didn’t find any alignments for the paired reads for each of the BGI mate-pair Illumina data:

  • 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

First, I ran QUAST with the default settings:

Interactive link: http://owl.fish.washington.edu/Athaliana/quast_results/results_2017_10_05_14_21_50/report.html

Using that Canu assembly with Redundans certainly seems to results in a better assembly.

Decided to run QUAST with the –scaffolds option to see what happened:

Interactive link: http://owl.fish.washington.edu/Athaliana/quast_results/results_2017_10_05_14_28_51/report.html

The scaffolds with the “Ns” removed from them are appended with “_broken” – meaning the scaffolds were broken apart into contigs. Things are certainly cleaner when using the --scaffolds option, however, as far as I can tell, QUAST doesn’t actually generate a FASTA file with the “_broken” scaffolds!

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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:

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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

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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

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Data Aggregation – Ava’s Complete Sample List

I received Ava’s master sheet of all the samples she collected for this project. I needed to aggregate a full list of the samples I’ve previously extracted DNA from, so that I can compare to her master sample list and generate a list of the remaining samples that I need to extract DNA from..

Here are the files I needed to work with (Google Sheets):

The files required multiple formatting steps in order to produce accession numbers that were formatted in the same fashion across all three sheets. This was needed in order to be able to successfully merge all of the sheets into a single sheet containing all of the data, which will make it easy to sort, and generate a list of samples that need to be extracted.

Text file manipulations were performed in a Jupyter notebook, which is linked below. All files were downloaded from Google Sheets as tab-delimited files prior to working on them.

Jupyter Notebook file: 20170831_ava_ab_samples_aggregation.ipynb
Jupter Notebook on NBviewer: 20170831_ava_ab_samples_aggregation.ipynb

Now that we have the tables formatted, we can use the accession number as a common field by which to combine the two tables. This will allow easy sorting and identification of the remaining samples that I need to extract. I’ll do this by using SQLite3.

Use SQLite3 (in Linux Ubuntu):

Change to directory containing files:

cd ~/Dropbox/Sam Friedman Lab/tmp

Start SQLite3:

sqlite3

Set field separator as tab-delimited:

.separator "t"

Create databases by importing files and providing a name for corresponding databases:

.import ava_master_ab_list_formatted.tsv master_list
.import Ava_WS_Transmission_DNA_Extractions_all.tsv extracted_list

Set output display mode to tabs:

.mode tabs

Set output display to include column headers:

.headers on

Set the output to write to a file instead of the screen:

.output 20170905_master_extraction_list.tsv

SELECT statement to combine the two tables:

SELECT * FROM (SELECT * FROM master_list UNION ALL SELECT * FROM extracted_list) s GROUP BY accession_number ORDER BY accession_number;

The SELECT statement above works in the following fashion:

Uses a sub-query (contained in the parentheses) that combines all of the rows in both tables and creates an intermediate table (that’s the s after the sub-query). Then, all of the columns in that intermediate table are selected by the initial SELECT * FROM and organized by the GROUP BY clause (which combines any rows with identical values in the accession_number column) and then sorts them with the ORDER BY clause.

After that’s finished, we want to reset the output to the screen so we don’t overwrite our file:

.output stdout

The output file is here (Google Sheet): ava_abalone_master_extraction_list

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Data Management – Olympia oyster UW PacBio Data from 20170323

Due to other priorities, getting this PacBio data sorted and prepped for our next gen sequencing data management plan (DMP) was put on the back burner. I finally got around to this, but it wasn’t all successful.

The primary failure is the inability to get the original data file archived as a gzipped tarball. The problem lies in loss of connection to Owl during the operation. This connection issue was recently noticed by Sean in his dealings with Hyak (mox). According to Sean, the Hyak (mox) people or UW IT ran some tests of their own on this connection and their results suggested that the connection issue is related to a network problem in FTR, and is not related to Owl itself. Whatever the case is, we need to have this issue addressed sometime soon…

Anyway, below is the Jupyter notebook that demonstrates the file manipulations I used to find, copy, rename, and verify data integrity of all the FASTQ files from this sequencing run.

Next up is to get these FASTQ files added to the DMP spreadsheets.

Jupyter notebook (GitHub): 20170622_oly_pacbio_data_management.ipynb

 

I’ve also embedded the notebook below, but it might be easier to view at the GitHub link provided above.

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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):

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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.

I’ve contacted them for more info.

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

Jupyter notebook (GitHub): 20170314_docker_Oly_BGI_GBS_demultiplexing_reproducibility.ipynb

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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

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