Data check on Oly BS-Seq samples

Posted by & filed under Ostrea lurida.

 

BS-seq_Libraries_for_Sequencing_at_Genewiz___Sam_s_Notebook_1C505006.png

For the 12 samples

Select 4 samples from 1NF gel take 2
Select 4 samples from 2NF gel take 2

Select 2 from gel take 2 Lotterhos
M1
M2
M3

Select 2 from the following sent to Katie (do not have to run on gel)
NF2 14
NF2 6
NF2 18
NF2 15
NF2 17

DNA_Isolation_–_Oly_gDNA_for_BS-seq___Sam_s_Notebook_1C50508C.png

Short term will just check out the first 8.

These are samples outplanted at Oyster Bay and Fidalgo, and in both cases parents from Fidalgo.

The hypothesis is that Epigenetic pattern will differ (and we can attribute to Environment)

Quick look at raw data

BS-seq_Libraries_for_Sequencing_at_Genewiz___Sam_s_Notebook_1C505191.png

http://onsnetwork.org/kubu4/category/olympia-oyster-reciprocal-transplant/bs-seq-libraries-for-genewiz/

Sequencing Platform: Illumina HiSeq2500

Read Type/Length: 50bp single-end, single index

Total Number of Reads: 116,280,817

Reads Per File:

11_GGCTAC_L001_R1_001.fastq.gz    10933121

12_CTTGTA_L001_R1_001.fastq.gz    10816647

1_ATCACG_L001_R1_001.fastq.gz    9402890

2_CGATGT_L001_R1_001.fastq.gz    11954873

3_TTAGGC_L001_R1_001.fastq.gz    11817358

4_TGACCA_L001_R1_001.fastq.gz    11606618

5_ACAGTG_L001_R1_001.fastq.gz    12589609

6_GCCAAT_L001_R1_001.fastq.gz    12489766

7_CAGATC_L001_R1_001.fastq.gz    10295293

8_ACTTGA_L001_R1_001.fastq.gz    14374642

Unzip

In [1]:
cd /Volumes/Histidine/hectocotylus/whole-BS-01

/Volumes/Histidine/hectocotylus/whole-BS-01
In [2]:
%%bash
for f in *.gz
do
  STEM=$(basename "${f}" .gz)
  gunzip -c "${f}" > /Volumes/Histidine/hectocotylus/whole-BS-01/fq/"${STEM}"
done

FastQC

In [3]:
!/Applications/bioinfo/FastQC/fastqc \
-o /Volumes/Histidine/hectocotylus/whole-BS-01/fq/ \
-t 4 \
/Volumes/Histidine/hectocotylus/whole-BS-01/fq/*

Started analysis of 1_ATCACG_L001_R1_001.fastq
Started analysis of 2_CGATGT_L001_R1_001.fastq
Started analysis of 3_TTAGGC_L001_R1_001.fastq
...
8_ACTTGA_L001_R1_001_fastq_FastQC_Report_1C50546C.png

this unusual pattern seem to hold true..

In [ ]:

 

Since you’ve been gone

Posted by & filed under Cgigas DNA Methylation, Ostrea lurida.

Soon after Ensenada I went to Chili, SICB, and PAG (in that order). The new year is often of time to let go of lingering projects, and likely I will be doing that soon. But to bring a few pending efforts to the forefront, so that I can analyze etc here is a bit of data that is (or soon will) be coming in.
Much of this is centered around the Ostrea lurida.

The first batch was 2bRAD data.
sheet

The full list of samples are here.

sc

These raw data are here.

A quick fastqc….

We also now have a fresh set of MBD-BS.. now out for sequencing.
Pregame here

pic

And just some plain old BS
pic

Details

From Ensenada and beyond

Posted by & filed under Panopea generosa.

There have not been many posts recently, but that is not to say I have not been doing any science. Much of what I have been doing is numerous burst commits on the Panopea transcriptome paper / project. This can be found @ https://github.com/sr320/paper-pano-go.

You can see all the Jupyter nbs in this sub-directory. I will highlight some of my proudest cell moments here:

Ok the first one is so good I will just give you the whole thing except that I will provide just a little more comments ##

!wc -l analyses/Geoduck-transcriptome-v2.tab
  154407 analyses/Geoduck-transcriptome-v2.tab

!head -5 analyses/Geoduck_v2_blastn-NT.out
comp190_c0_seq1 gi  315593157   gb  CP002417.1      84.50   200 31  0   1   200 2271015 2271214 1e-47     198   Bacteria    Variovorax paradoxus EPS, complete genome   595537  Variovorax paradoxus EPS    Variovorax paradoxus EPS    b-proteobacteria
comp1900_c0_seq1    gi  481319564   gb  CP003293.1      100.00  271 0   0   1   271 1334370 1334640 1e-138    501   Bacteria    Propionibacterium acnes HL096PA1, complete genome   1134454 Propionibacterium acnes HL096PA1    Propionibacterium acnes HL096PA1    high GC Gram+
comp2164_c0_seq1    gi  221728669   gb  CP001392.1      98.47   261 4   0   1   261 721134  721394  2e-126    460   Bacteria    Acidovorax ebreus TPSY, complete genome 535289  Acidovorax ebreus TPSY  Acidovorax ebreus TPSY  b-proteobacteria
comp2742_c0_seq1    gi  527256352   ref XM_005146392.1      85.65   230 33  0   16  245 2293    2522    7e-61     243   Eukaryota   PREDICTED: Melopsittacus undulatus exostosin-like glycosyltransferase 3 (EXTL3), mRNA   13146   Melopsittacus undulatus budgerigar  birds
comp3315_c0_seq1    gi  156627645   gb  AC209228.1      79.13   206 36  6   3   202 76584   76380   1e-28     135   Eukaryota   Populus trichocarpa clone POP075-L19, complete sequence 3694    Populus trichocarpa black cottonwood    eudicots

#Lets subset above table to non Eukaryotes
#Here I am letting awk know we are dealing with tabs, 
#and I want to have all rows where column 17 is NOT Eukaryota.
!awk -F"\t" '$17 != "Eukaryota" {print $1, $17 ,$15}' analyses/Geoduck_v2_blastn-NT.out \
> analyses/Non-Eukaryota-Geoduck-v2

!sort analyses/Non-Eukaryota-Geoduck-v2 > analyses/Non-Eukaryota-Geoduck-v2.sorted

!sort analyses/Geoduck-transcriptome-v2.tab > analyses/Geoduck-transcriptome-v2.sorted

!head -2 analyses/Geoduck-transcriptome-v2.sorted
comp100000_c0_seq1      TGAATGTATGTTTGTGAACGTATGTATATGAATGTATGTATGTGAATGCATACCATCTGTATAAGTATAATCCGACCGGGAGATGTTTATTCACACAGTTTGGCATTATGACGTTTAACCTCTGAATTGGCGTCTCTTGCTACTGACCGCTTCACAGTGATGACCCATGTTGTCACTTCTAATCTATTTATGTATTGCTCTTTTATATTATACTATTAACGCTGTTAAAATACACTACCGCTAAACAAATAAGAGTTGTGGGTTTGAATCATTGGTGAGTGCAGGAACCTCAGCAGGTCATTAAGATTTACGTGTACGTCTATCCTAAACCTACATGTTTCAACTTTGTTGTTTTTCGGTTTCGTTCTCTGTACACAGCTCTTGAAACATACATAAAATACCATTTTATTAAAAAATATGTCTCTATTTAATGTTAAAACCTTTTTAAGAAAA
comp100001_c1_seq1      GCTTTACCAGTTGTTACAAACATTTTAATAGTTATAGTTAATATACACAACATTTTAAATTAACTAGTGTCGAGAACTTGAGTCGGACATAGAGAATTAAATGTTTGTTGAACTTTAGCCAAGCACTTTTATTCTATTACTTTTTGAAGTATTTAATACCTTAAAATAATGGAATACTCCTGTAGAGTCCTTGAAGCCATCAACAATTTACCAACCTCCAAATAAAATATGAATATATTTTACATGATGAATTTACATAATGGATATATCATTGATATCTGCCAAGTTAACTTCACCTACCATTTTTAAGCTTACTTTGACCATGTTAGTTGGTATTGTGTATATAACGAGTGGGAGGACATTCATACCTGGCATTTGTTTGGTCAAACTGACACAAGATTTATGTTTATTTCAAACCTATATATAAAACAAGTCTCAATGAATATCTTCCTAGGCACAAGACAATGCTGATAAAATGTCTTGTTCAAGGACA


# joining with -v suppressing joined lines
!join -v 1 analyses/Geoduck-transcriptome-v2.sorted \
analyses/Non-Eukaryota-Geoduck-v2.sorted | wc -l
  153982

!join -v 1 analyses/Geoduck-transcriptome-v2.sorted \
analyses/Non-Eukaryota-Geoduck-v2.sorted \
> ../data-results/Geoduck-transcriptome-v3.tab

!head -2 ../data-results/Geoduck-transcriptome-v3.tab
comp100000_c0_seq1 TGAATGTATGTTTGTGAACGTATGTATATGAATGTATGTATGTGAATGCATACCATCTGTATAAGTATAATCCGACCGGGAGATGTTTATTCACACAGTTTGGCATTATGACGTTTAACCTCTGAATTGGCGTCTCTTGCTACTGACCGCTTCACAGTGATGACCCATGTTGTCACTTCTAATCTATTTATGTATTGCTCTTTTATATTATACTATTAACGCTGTTAAAATACACTACCGCTAAACAAATAAGAGTTGTGGGTTTGAATCATTGGTGAGTGCAGGAACCTCAGCAGGTCATTAAGATTTACGTGTACGTCTATCCTAAACCTACATGTTTCAACTTTGTTGTTTTTCGGTTTCGTTCTCTGTACACAGCTCTTGAAACATACATAAAATACCATTTTATTAAAAAATATGTCTCTATTTAATGTTAAAACCTTTTTAAGAAAA
comp100001_c1_seq1 GCTTTACCAGTTGTTACAAACATTTTAATAGTTATAGTTAATATACACAACATTTTAAATTAACTAGTGTCGAGAACTTGAGTCGGACATAGAGAATTAAATGTTTGTTGAACTTTAGCCAAGCACTTTTATTCTATTACTTTTTGAAGTATTTAATACCTTAAAATAATGGAATACTCCTGTAGAGTCCTTGAAGCCATCAACAATTTACCAACCTCCAAATAAAATATGAATATATTTTACATGATGAATTTACATAATGGATATATCATTGATATCTGCCAAGTTAACTTCACCTACCATTTTTAAGCTTACTTTGACCATGTTAGTTGGTATTGTGTATATAACGAGTGGGAGGACATTCATACCTGGCATTTGTTTGGTCAAACTGACACAAGATTTATGTTTATTTCAAACCTATATATAAAACAAGTCTCAATGAATATCTTCCTAGGCACAAGACAATGCTGATAAAATGTCTTGTTCAAGGACA

#Going from tab back to fasta!
!awk '{print ">"$1"\n"$2}' ../data-results/Geoduck-transcriptome-v3.tab \
> ../data-results/Geoduck-transcriptome-v3.fa

!fgrep -c ">" ../data-results/Geoduck-transcriptome-v3.fa
153982

tldr:

'$c != string`  join -v  awk '{print ">"$1"\n"$2}'

One more tidbit- I wanted to see how many blast hits were in the opposite direction "- frame".

thus:

!awk '($10-$9) < 0 {print $1, "\t", ($10-$9)}' \
../data-results/Geoduck-tranv2-blastx_sprot.tab \
> analyses/Geoduck-tranv2-minus_direction.tab
!head analyses/Geoduck-tranv2-minus_direction.tab
!wc -l analyses/Geoduck-tranv2-minus_direction.tab

comp95_c0_seq1   -230
comp146_c0_seq1      -173
comp195_c0_seq1      -185
comp296_c0_seq1      -200
comp455_c1_seq1      -197
comp943_c0_seq1      -218
comp1059_c0_seq1     -227
comp1683_c0_seq1     -206
comp1868_c0_seq1     -308
comp1910_c1_seq1     -248
   10413 analyses/Geoduck-tranv2-minus_direction.tab

Re-Reproducing differential methylation analyses, again

Posted by & filed under Cgigas DNA Methylation.

Having just given a talk on reproducibility, I am in the midst of responding to reviewer comments about what we did (12 months ago!) and boy can I say every minute of putting this notebook together was worth it. I even found where we ran the entire notebook, so all result files are easily accessible. Beyond praising Claire, I will document my follow up analysis here.

Essentially the want more quantitative information on differential methylation beyond ..

OlsonandRoberts2015_9_docx_1BC2FBAE.png

Makes sense.

Here is what was originally done.

olson-ms-nb_BiGo_dev_ipynb_at_master_·_che625_olson-ms-nb_1BC2FCC5.png

For example the file named linexon contained 16 exon_intersect_DML_lin_u.txt. The 4 files were concatenated to produce lintable ….

eagle_fish_washington_edu_cnidarian_olson-ms-nb-master_12_1714_wd_lintable_1BC2FE30.png

and a little awk

awk 'FNR==NR{sum+=$1;next}; {print $0,sum}' lintable{,} > lin_total
awk '{print $2, $1, $3, (($1/$3)*100)}' lin_total > lineage_DMLs

to create lineage_DMLs…

eagle_fish_washington_edu_cnidarian_olson-ms-nb-master_12_1714_wd_lineage_DMLs_1BC2FE65.png

Analogously here are the developmental_DMLs….

eagle_fish_washington_edu_cnidarian_olson-ms-nb-master_12_1714_wd_developmental_DMLs_1BC3F775.png

And we certainly need to now how many all_CGs we have…

eagle_fish_washington_edu_cnidarian_olson-ms-nb-master_12_1714_wd_all_CGs_1BC3F807.png

Table

Feature Family specific DMLs Developmental specific DMLs
Transposable Element 17 16
Promoter Region 2 3
Exon 16 12
Intron 25 46

I know we did this before, but I believe the reviewers want a break-down, or list of which specific transposable elements. This is a long shot if I can find this…
2 minutes later https://github.com/sr320/ipython_nb/blob/master/BiGo_larvae_manuscript4.ipynb.

To be sure files are accurate, I will intersectbed again. Based on recollection there is likely not a difference in proportion based on all TEs. This brings up a an important point of how to record “negative” data that does not go into a paper.

Using ESearch and EFetch to retrieve data

Posted by & filed under Tutorial.

Lets say there was this person, we will call her Emma for now, that needed to download lots of data but wanted to make it more robust and reliable. Here is a way to use NCBI ESearch and EFetch tools to do so. Complete documention at http://www.ncbi.nlm.nih.gov/books/NBK25498/. Specific example used is here

Example one: Download all ilumatobacter protein sequences in fasta format.

Will use Esearch to get GI numbers, post them to history and multiple EFetch calls to retrieve data.

Input: $query – ilumatobacter[orgn]

Output: A file named “ilumatobacter.fa” containing FASTA data.

Perl script


use LWP::Simple;
$query = 'ilumatobacter[orgn]';

#assemble the esearch URL
$base = 'http://eutils.ncbi.nlm.nih.gov/entrez/eutils/';
$url = $base . "esearch.fcgi?db=protein&term=$query&usehistory=y";

#post the esearch URL
$output = get($url);

#parse WebEnv, QueryKey and Count (# records retrieved)
$web = $1 if ($output =~ /(\S+)<\/WebEnv>/);
$key = $1 if ($output =~ /(\d+)<\/QueryKey>/);
$count = $1 if ($output =~ /(\d+)<\/Count>/);

#open output file for writing
open(OUT, ">ilumatobacter.fa") || die "Can't open file!\n";

#retrieve data in batches of 500
$retmax = 500;
for ($retstart = 0; $retstart < $count; $retstart += $retmax) {
$efetch_url = $base ."efetch.fcgi?db=protein&WebEnv=$web";
$efetch_url .= "&query_key=$key&retstart=$retstart";
$efetch_url .= "&retmax=$retmax&rettype=fasta&retmode=text";
$efetch_out = get($efetch_url);
print OUT "$efetch_out";
}
close OUT;

So if you wanted to use this simple paste the above code in text file (Suggest using TextWrangler) and saving as .pl file (ie /Users/sr320/Desktop/ill-prot.pl. Then in Terminal, type perl /Users/sr320/Desktop/ill-prot.pl. The data will download to whatever directory you are in Terminal.

In actuallity, this still seems to fail randomly. This is common to see on the internets. The best guess is too many requests during busy time of day, so it might take a couple if trys. See http://www.ncbi.nlm.nih.gov/books/NBK25497/#chapter2.Usage_Guidelines_and_Requiremen for usage recommendations.

Running the numbers

Posted by & filed under Ostrea lurida.

Today I brought another 16 samples into the DNA extraction pipeline. These were subjected to 30 minute ProK digestion at 60C as opposed to overnight.

IMG_3073_JPG_1BAB1123.png

It became clear the number scheme was complicated thus I converted to 1-32. Will keep this running fwd and cross-referencing.

IMG_3074_JPG_1BAB11C5.png

In the future the number scheme will be simpler, but here is the actual run down.

IMG_3068_JPG_1BAB15E3.png

Approximately 300ul of aqueous solution was recovered and taken through the protocol by Sam.

First look seems pretty good.
DNA_Isolation_–_Olympia_oyster_whole_body___Sam_s_Notebook_1BAB13A3.png

Sam will run a gel tomorrow. Note the Katherine is a bit ahead of us, but we have something to compare to.

Upon improving extractions

Posted by & filed under Ostrea lurida.

In an attempt to determine most efficient means to get some high quality DNA from the archived oyster samples – a suite of samples were started out with M1 buffer and ProK.

The first ‘unit’ to go through will be the April Dabob samples. For today, I took 16 samples from the Hood canal population (code 3H13-16; yellow tubes). Samples were homogenized with plastic mortar and the other 8 did not get a mortar poke.
IMG_3064_JPG_and_Photo_-_Google_Photos_1BA8F645.png
All samples were vortexed and placed at 37C at 2pm. This was done with air incubator, non-shaking.

Tomorrow, an additional set of samples will undergo a shorter Proteinase K digestion, subjected to Mollusc DNA extraction kit to try to determine best way to scale up.

Photo_-_Google_Photos_1BA8F73F.png

A few outliers remaining

Posted by & filed under Ostrea lurida.

Integrating last bit of qPCR data into master datasheet. This includes 5 runs post 8/15.

Resilience-Project_qPCR_data_at_master_·_jheare_Resilience-Project_1B91E568.png

likely error above – second EF1 was 18s test.

CARM
Looks nice, corrected…

Screenshot_8_29_15__6_14_AM_1B91E7B1.png

Elong factor
Correction is fine, but mechanical reps have some issues
Screenshot_8_29_15__6_28_AM_1B91EB21.png

As noticed in last batch of analysis these need to be checked at raw data level

Screenshot_8_29_15__6_32_AM_1B91EBFF.png

28s
Screenshot_8_29_15__6_40_AM_1B91EDBE.png

Thoughts on normalizing gene…
Noting that one EF1 rep was thrown out for mechanical (see above). Here is a crude look at EF1, actin, and 28s respectively…

Screenshot_8_29_15__6_46_AM_1B91EF43.png

Sifting Sands

Posted by & filed under Miscellaneous, Workflows.

Below is a quick workflow I am using to help Drinan annotate ~1.5 million sequences from an amplicon targeting NGS effort of sand.

 head /Users/sr320/Dropbox/hummingbird-ipython-nbs/data/DanD/meiofauna_forward_sequences.fa
 >M02215:33:000000000-AFA9E:1:1101:14961:2005 1:N:0:15
 TGACTGTGCTAAGGTAGCATAATTAATTGTCTTTTAATTAGAGACTTGTTTGAAAGATTT
 TTTGAATTTAATATAGTTTTAAAATTATAAAAATGAATTTTTATATATTGGTAAAAATAC
 CATGATTTTTTAAAAAGACGATAAGACCCTATCAAGTTTTACTTAAATTTAAAGAAAATT
 TAGGTTTTAATGGGGCATTATTATTTATTTTAAATAAATTTTGATCTTAAATTAAATTTT
 AGGAAATTTAATAAAATTACTGTAGGGATAACAGTGTAATATTTTTTAAAGTTCATATTT
 A
 >M02215:33:000000000-AFA9E:1:1101:11050:2011 1:N:0:15
 TAACTGTGCTAAGGTAGCATAATCACTTGTCTCCTAATTAGAGACTGGCATGAAAGGGTA
 AACTCTTTATAACTTTATAAAGCATACACACTGAAATTTTTATTTAGACGAAGAAATCTA

 

 

 

Within a given working directory I proceeded to (in Jupyter NB)

cp meiofauna_forward_sequences.fa query.fasta – to simply rename.

!/Users/steven/Dropbox/hummingbird-ipython-nbs/script-box/fasta-splitter.pl \
--n-parts 20 \
query.fasta – to split. This was if failure occurs, simple restart.

a little magic

 %%bash
 for f in query.part*
 do
 blastn \
 -query $f \
 -db /Volumes/Data/blast_db/nt \
 -evalue 1e-5 \
 -max_target_seqs 1 \
 -max_hsps 1 \
 -outfmt "6 std sskingdoms stitle staxids sscinames scomnames sblastnames" \
 -num_threads 14 \
 -out blastout_"$f"_nt
 done

 

Spiced it up with output format-

from manual:

 

 Options 6, 7, and 10 can be additionally configured to produce
 a custom format specified by space delimited format specifiers.
 The supported format specifiers are:
 qseqid means Query Seq-id
 qgi means Query GI
 qacc means Query accesion
 qaccver means Query accesion.version
 qlen means Query sequence length
 sseqid means Subject Seq-id
 sallseqid means All subject Seq-id(s), separated by a ';'
 sgi means Subject GI
 sallgi means All subject GIs
 sacc means Subject accession
 saccver means Subject accession.version
 sallacc means All subject accessions
 slen means Subject sequence length
 qstart means Start of alignment in query
 qend means End of alignment in query
 sstart means Start of alignment in subject
 send means End of alignment in subject
 qseq means Aligned part of query sequence
 sseq means Aligned part of subject sequence
 evalue means Expect value
 bitscore means Bit score
 score means Raw score
 length means Alignment length
 pident means Percentage of identical matches
 nident means Number of identical matches
 mismatch means Number of mismatches
 positive means Number of positive-scoring matches
 gapopen means Number of gap openings
 gaps means Total number of gaps
 ppos means Percentage of positive-scoring matches
 frames means Query and subject frames separated by a '/'
 qframe means Query frame
 sframe means Subject frame
 btop means Blast traceback operations (BTOP)
 staxids means unique Subject Taxonomy ID(s), separated by a ';'
 (in numerical order)
 sscinames means unique Subject Scientific Name(s), separated by a ';'
 scomnames means unique Subject Common Name(s), separated by a ';'
 sblastnames means unique Subject Blast Name(s), separated by a ';'
 (in alphabetical order)
 sskingdoms means unique Subject Super Kingdom(s), separated by a ';'
 (in alphabetical order)
 stitle means Subject Title
 salltitles means All Subject Title(s), separated by a '<>'
 sstrand means Subject Strand
 qcovs means Query Coverage Per Subject
 qcovhsp means Query Coverage Per HSP
 When not provided, the default value is:
 'qseqid sseqid pident length mismatch gapopen qstart qend sstart send
 evalue bitscore', which is equivalent to the keyword 'std'

 

 

and sample output:

 

M02215:33:000000000-AFA9E:1:1103:16706:22078 gi|56550013|gb|AY803660.1| 85.62 160 17 6 145 299 30 188 7e-37 163 Eukaryota Hydrothelphusa madagascariensis 18S ribosomal RNA gene, partial sequence 168669 Hydrothelphusa madagascariensis Hydrothelphusa madagascariensis crustaceans
M02215:33:000000000-AFA9E:1:1103:16318:23659 gi|393186538|gb|JX083886.1| 82.50 280 37 11 25 297 1 275 2e-58 235 Eukaryota Microeuraphia sp. 1 MPL-2012 isolate KACb37 16S ribosomal RNA gene, partial sequence; mitochondrial 1204330 Microeuraphia sp. 1 MPL-2012 Microeuraphia sp. 1 MPL-2012 crustaceans
M02215:33:000000000-AFA9E:1:1103:23857:24453 gi|386786433|gb|JQ435298.1| 96.23 53 2 0 248 300 1545 1597 5e-14 87.9 Eukaryota Cf. Traiania sp. DNA106167 voucher MCZ DNA106167 28S ribosomal RNA gene, partial sequence 1183147 cf. Traiania sp. DNA106167 cf. Traiania sp. DNA106167 daddy longlegs
M02215:33:000000000-AFA9E:1:1103:12121:24652 gi|302140702|gb|GQ343306.1| 98.31 295 5 0 6 300 22 316 1e-143 518 Eukaryota Evadne nordmanni isolate E37/5 16S ribosomal RNA gene, partial sequence; mitochondrial 141403 Evadne nordmanni Evadne nordmanni crustaceans
M02215:33:000000000-AFA9E:1:1104:23888:3062 gi|94960351|gb|DQ467789.1| 98.25 57 1 0 1 57 60 116 6e-18 100 Eukaryota Lynceus macleyanus isolate 53 16S ribosomal RNA gene, partial sequence; mitochondrial 381959 Lynceus macleyanus Lynceus macleyanus crustaceans
M02215:33:000000000-AFA9E:1:1104:11382:3077 gi|343168999|gb|JN018352.1| 84.52 310 36 9 1 300 2729 3036 7e-77 296 Eukaryota Ischyropsalis pyrenaea voucher MNHN-JAA33 28S ribosomal RNA gene, partial sequence 1046795 Ischyropsalis pyrenaea Ischyropsalis pyrenaea daddy longlegs
M02215:33:000000000-AFA9E:1:1104:16849:3145 gi|472441035|gb|KC529449.1| 86.77 310 29 12 1 301 558 864 1e-88 335 Eukaryota Microdalyellia nanella isolate W43ss 18S ribosomal RNA gene, partial sequence 1311903 Microdalyellia nanella Microdalyellia nanella flatworms
M02215:33:000000000-AFA9E:1:1104:15899:3944 gi|349592295|gb|JN205453.1| 92.12 292 21 1 12 301 1 292 2e-111 411 N/A Uncultured organism clone KCON28S38 28S ribosomal RNA gene, partial sequence 155900 uncultured organism uncultured organism N/A
M02215:33:000000000-AFA9E:1:1104:14928:4021 gi|46812207|gb|AY569664.1| 87.10 310 23 15 1 301 574 875 1e-88 335 Eukaryota Arenicolides ecaudata 18S ribosomal RNA gene, partial sequence 273060 Arenicolides ecaudata Arenicolides ecaudata segmented worms
M02215:33:000000000-AFA9E:1:1104:19218:4051 gi|301178327|gb|HM799910.1| 96.47 85 2 1 1 85 550 633 1e-29 139 Eukaryota Uncultured marine metazoan clone PRTBE7499 small subunit ribosomal RNA gene, partial sequence 329654 uncultured marine metazoan uncultured marine metazoan animals
M02215:33:000000000-AFA9E:1:1104:19414:4162 gi|295805440|emb|FN389538.1| 95.24 84 3 1 1 84 21 103 2e-27 132 Eukaryota Uncultured Glomus 18S ribosomal RNA gene, isolate, partial sequence. 07ED8BM 231055 uncultured Glomus uncultured Glomus glomeromycetes
M02215:33:000000000-AFA9E:1:1104:18162:4373 gi|342210101|gb|JF277589.1| 90.52 306 21 7 1 299 67 371 2e-107 398 Eukaryota Nemertean sp. 2 SA-2011 voucher MCZ DNA106139 16S ribosomal RNA gene, partial sequence; mitochondrial 947588 Nemertean sp. 2 SA-2011 Nemertean sp. 2 SA-2011 ribbon worms
M02215:33:000000000-AFA9E:1:1104:25946:4764 gi|110535863|gb|DQ665998.1| 84.92 305 37 9 1 301 552 851 5e-78 300 Eukaryota Phagocata vitta 18S ribosomal RNA gene, complete sequence 391283 Phagocata vitta Phagocata vitta flatworms
M02215:33:000000000-AFA9E:1:1104:26451:5022 gi|307647639|gb|HM564573.1| 93.71 302 18 1 1 301 470 771 1e-123 451 Eukaryota Phanodermatidae sp. JCC52 18S ribosomal RNA gene, partial sequence 883396 Phanodermatidae sp. JCC52 Phanodermatidae sp. JCC52 nematodes
M02215:33:000000000-AFA9E:1:1104:14581:5493 gi|472441081|gb|KC529495.1| 86.36 308 33 9 1 301 559 864 2e-86 327 Eukaryota Dochmiotrema limicola isolate UH80.2 18S ribosomal RNA gene, partial sequence 1311982 Dochmiotrema limicola Dochmiotrema limicola flatworms
M02215:33:000000000-AFA9E:1:1104:9527:5655 gi|170672378|gb|EU376009.1| 87.06 309 32 6 1 301 2834 3142 9e-91 342 Eukaryota Craterostigmus tasmanianus 28S ribosomal RNA gene, partial sequence 60162 Craterostigmus tasmanianus Craterostigmus tasmanianus centipedes
M02215:33:000000000-AFA9E:1:1104:15515:5664 gi|373860124|gb|HQ865054.1| 86.75 302 34 6 2 301 7 304 2e-87 331 Eukaryota Uncultured eukaryote clone SGPX651 18S ribosomal RNA gene, partial sequence 100272 uncultured eukaryote uncultured eukaryote eukaryotes
M02215:33:000000000-AFA9E:1:1104:25827:5676 gi|154101573|gb|EF552055.1| 99.34 301 2 0 1 301 68 368 6e-152 545 Eukaryota Balanus glandula isolate 2 16S ribosomal RNA gene, partial sequence; mitochondrial 110520 Balanus glandula Balanus glandula crustaceans
M02215:33:000000000-AFA9E:1:1104:22156:5763 gi|157787506|gb|EF990727.1| 89.11 303 27 4 1 300 2446 2745 1e-99 372 Eukaryota Rhabditoides inermiformis strain SB328 28S large subunit ribosomal RNA gene, partial sequence 96653 Rhabditoides inermiformis Rhabditoides inermiformis nematodes
M02215:33:000000000-AFA9E:1:1104:14633:5939 gi|213959396|gb|FJ426630.1| 78.43 306 53 12 1 301 47 344 4e-44 187 Eukaryota Brachionus angularis voucher S. H. Cheng 001 16S ribosomal RNA gene, partial sequence; mitochondrial 396692 Brachionus angularis Brachionus angularis rotifers
M02215:33:000000000-AFA9E:1:1104:10324:6174 gi|386696111|gb|JQ000284.1| 79.22 308 52 11 2 301 537 840 4e-49 204 Eukaryota Xolalgidae gen. sp. AD1204 18S ribosomal RNA gene, partial sequence 1111400 Xolalgidae gen. sp. AD1204 Xolalgidae gen. sp. AD1204 mites &amp; ticks
M02215:33:000000000-AFA9E:1:1104:13062:6188 gi|74100231|gb|DQ186202.1| 87.97 291 25 7 1 284 13348 13635 1e-88 335 Eukaryota Thalassiosira pseudonana mitochondrion, complete genome 35128 Thalassiosira pseudonana Thalassiosira pseudonana diatoms
M02215:33:000000000-AFA9E:1:1104:13172:6214 gi|109390598|emb|AM039747.1| 82.95 305 39 13 1 298 2492 2790 7e-67 263 Eukaryota Heligmosomoides polygyrus 28S rRNA gene 6339 Heligmosomoides polygyrus Heligmosomoides polygyrus nematodes

Will end up cating 20 output files once done.

Finishing out with the mechanical

Posted by & filed under Ostrea lurida.

Currently there is a pretty robust spreadsheet and over the past few days Jake has cranked through some reps to see how the oysters that were mechanically stressed hold up. Below is how these data are integrated.

Jake_Heare_Research_Central__8_10_2015_EF1d_Mech_Stress_2_rep_qPCR_1B821B44.png

Currently the 8-10 samples (yellow) have been skipped, but we might have a look.

First up is having a look at the new HSP 70 reps. The mechanical data still needs some better resolution. Hopefully teh 8-10 samples migh shed some light.

Screenshot_8_17_15__9_53_AM_1B821ECC.png

Next up is two more reps of PGEEP4.
Looks good, and given the doubling of reps we could easily drop ‘outlier’ runs and still have triplicates, tight triplicates.

Screenshot_8_17_15__10_15_AM_1B822404.png

GRB2… now good to go, with the first pair of reps dead on.

BMP2…. could use some help from the other mechanical stress runs
Screenshot_8_17_15__11_03_AM_1B822F48.png

TLR….seemed like a relatively easy fix (besides no detection) in that just needed to correct for machine.
Screenshot_8_17_15__11_10_AM_1B8230E7.png

And the correction indicating the fact that expression was so low, only able to be detected by Opticon
Screenshot_8_17_15__11_17_AM_1B82327D.png

The 8-15 runs had minimal control and temp samples with mechanical run in dups.

Screenshot_8_17_15__11_24_AM_1B82344E.png

This needs a little carressing before integrating into data.
This should be in two columns with empty cells where no samples were run- in this order.

H_C_1
H_C_2
H_C_3
H_C_4
H_C_5
H_C_6
H_C_7
H_C_8
N_C_1
N_C_2
N_C_3
N_C_4
N_C_5
N_C_6
N_C_7
N_C_8
S_C_1
S_C_2
S_C_3
S_C_4
S_C_5
S_C_6
S_C_7
S_C_8
H_T_1
H_T_2
H_T_3
H_T_4
H_T_5
H_T_6
H_T_7
H_T_8
N_T_1
N_T_2
N_T_3
N_T_4
N_T_5
N_T_6
N_T_7
N_T_8
S_T_1
S_T_2
S_T_3
S_T_4
S_T_5
S_T_6
S_T_7
S_T_8
H_M_1
H_M_2
H_M_3
H_M_4
H_M_5
H_M_6
H_M_7
H_M_8
N_M_1
N_M_2
N_M_3
N_M_4
N_M_5
N_M_6
N_M_7
N_M_8
S_M_1
S_M_2
S_M_3
S_M_4
S_M_5
S_M_6
S_M_7
S_M_8

8-15 run update

Actin
Screenshot_8_17_15__3_55_PM_1B8273B6.png

Mechanical looks decent after correcting.

However taken together, bothersome the difference in crude expression levels.
Screenshot_8_17_15__3_57_PM_1B827441.png

Carm

Had some wet works issues

Jake_Heare_Research_Central__8_15_2015_CARM_CTM2_reps_qPCR_1B8274C8.png

H2AV
Assuming correction is correct- still a big differences in mechanincal here- could be real.

Screenshot_8_17_15__4_11_PM_1B8277A0.png

PGRP
No correction required as these were run on cfx, downside is some reps are not detected that would have been picked up with Opticon.

Do not see be shift in expression of mechanical stressed.
Screenshot_8_17_15__4_18_PM_1B827900.png

CRAF
Easy correction but skeptical of some very, very low Cts

Screenshot_8_17_15__4_25_PM_1B827AA2.png