The Trinity assembly is complete. Today I inspected it using transrate, in addition to running blast over the weekend to annotate genes using the Uniprot/Swissprot database. Transcriptome assembly quality, as per transrate using the trimmed/normalized reads, seem sub-par, as percent good mapping is only 10% … but I’ll investigate further. Check out my Jupyter notebook for more details: transcriptome-assess-annotate.ipynb
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I’ve migrated my notebook to here:
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These heatmaps were created by using the BP-FAT file created by DAVID when I entered my uniquely clustered proteins with a background of all detected proteins (in silos 3 and 9).
These heatmaps show how the protein abundances change over time for proteins whose Uniprot accession codes are associated with enriched BPs represented by parent terms which were given by DAVID during enrichment analysis.
I also included the heatmaps clustered by time below the first pair of plots.
The patterns of abundance seem to be very similar between the two silos, but silo 3 tends to have higher abundances than silo 9 except with platelet degranulation.
The days are clustered in the plots below. If we look only until the second node, we see three main groups for Silo 3:
and two main groups for Silo 9:
Based on this, we can see that protein abundance patterns are different between the silos, but we knew this already since these proteins were selected based on differential clustering before. The new information we can see is how the processes the proteins are linked to shift based on time.
This was done with the BP-FAT file because it had the fewest enriched processes and would be the easiest to work with and view as a test attempt. Here is the code I made.
Today I got comfortable using the Mox (Hyak) supercomputer, created my directories, and queued a transcriptome assembly using Trinity.
For details, please see my notebook: trinity-setup-on-mox.md
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Today I downloaded RNASeq data – four fastq files – from Olympia oyster pooled gonad. The gonad was from Fidalgo Bay and Oyster Bay oysters following a 2017 low pH exposure. I unzipped the files, then tested a couple methods of trimming and plotting quality scores for trimmed/untrimmed files.
Jupyter notebook to download/trim files: Inspecting fastq files.ipynb
RMarkdown notebook to run a program to extract and plot quality scores against bp for trimmed/untrimmed files: RNASeq-screening.md
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Proceeded with reverse transcription of Ronit’s DNased ctenidia RNA (from 20181016).
Reverse transcription was performed using 100ng of each sample with M-MMLV Reverse Transcriptase from Promega.
Briefly, 100ng of DNased RNA was combined with oligo dT primers and brought up to a final volume of 15uL. Tubes were incubated for 5mins at 70oC in a PTC-200 thermal cycler (MJ Research), using a heated lid. Samples were immediately placed on ice.
A master mix of buffer, dNTPs, water, and M-MMLV reverse transcriptase was made, 10uL of the master mix was added to each sample, and mixed via finger flicking. Samples were incubated for 1hr at 42oC in a PTC-200 thermal cycler (MJ Research), using a heated lid, followed by a 5min incubation at 65oC.
Samples were stored on ice for use later this afternoon by Ronit.
Samples will be stored in Ronit’s -20oC box.
Reverse transcription calcs (Google Sheet):
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Sample sheet (Google Sheet):
Samples were stored in -80oC:
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