Sam’s Notebook: Genome Feature Counts – Panopea-generosa-vv0.74.a4

In preparation for a paper we’re writing, we needed some summary stats for Panopea-generosa-vv0.74.a4. This info will be compiled in to a table for the manuscript. See our Genomic Resources wiki for more info on GFFs:

• Genomic Resources Wiki (GitHub)

Calculations were performed using Python in Jupyter Notebooks.

Genome Features Jupyter Notebook (GitHub):

• 20191029_swoose_pgen_v074.a4_genome_feature_counts.ipynb

Repeat Features Jupyter Notbooke (GitHub):

• 20191029_swoose_pgen_v074.a4_genome_repeats_counts.ipyn

Shelly’s Notebook: Thur. Oct. 24, Geoduck DMR filtering

This analysis is a follow up to Wed. Oct 23 analysis

Rerun DMRfind with different parameters

Reran DMRfind with different MCmax settings (this specifies what a differentially methylated site (DMS) is; it allows loci to not be exactly be overlapping but be within a window to be considered a DMS which helps for low coverage samples). This window size is defined by MCmax:

• mox scripts here for the following MCmax window sizes:
  • 50bp (analysis from Oct 23): 20191023_DMRfindAllEPI.sh
  • 30bp: 20191024_DMRfindAllEPI.sh
  • 25bp: 20191024_DMRfind_allEPI_25bp.sh
  • 10bp: 20191024_DMRfind_allEPI_10bp.sh
• output files here:
  • 50bp: https://gannet.fish.washington.edu/metacarcinus/Pgenerosa/analyses/20191023/
  • 30bp: https://gannet.fish.washington.edu/metacarcinus/Pgenerosa/analyses/20191024/
  • 25bp: https://gannet.fish.washington.edu/metacarcinus/Pgenerosa/analyses/20191024_25bp/
  • 10bp: https://gannet.fish.washington.edu/metacarcinus/Pgenerosa/analyses/20191024_10bp/

Validate DMR bed files in IGV

• loaded the following bed files into IGV:
  • 50bp: amb_AllTimes_DMR250bp_MCmax50_cov5x_rms_results_collapsed.tsv.DMR.bed
  • 30bp: amb_AllTimes_DMR250bp_MCmax30_cov5x_rms_results_collapsed.tsv.DMR.bed
  • 25bp: amb_AllTimes_DMR250bp_MCmax25_cov5x_rms_results_collapsed.tsv.DMR.bed
  • 10bp: amb_AllTimes_DMR250bp_MCmax10_cov5x_rms_results_collapsed.tsv.DMR.bed
  • bam files: amb all times filtered bam files
  • IGV session here: amb_AllTimes_IGV_20191024.xml
• Interestingly it seems there are some DMRs that make sense and are identified by all parameter settings like this example: and
• Then there are some DMRs that make sense and are only identified by one parameter setting like this example:
• Then some DMRs that are only identified by one setting and don’t make sense like this example:

CONCLUSIONS

• Try running group stats on % methylation data and see if it excludes DMRs that don’t make sense
  • Yupeng confirmed that methylpy only runs statistics on within sample data, not on group data. So I need to apply an ANOVA or GLM to determine DMRs that are statistically different between groups

Grace’s Notebook: Day 12 RNA extractions – RNA in all samples!

Today I did some more extractions, but moved on to day 12. I did a mix of all groupings (temperature and infection statuses). All extracted samples had detectable RNA! Details in post. Additionally, I’ll give a summary table of what all I have extracted so far.

Samples extracted today:

FRP	trtmnt_tank	sample_day	infection_status	maturity	tube_number
6137	ambient	12	1	I	301
6122	ambient	12	1	I	325
6125	ambient	12	1	I	303
6176	ambient	12	0	M	329
6179	ambient	12	0	M	315
6213	ambient	12	0	I	310
6104	cold	12	0	I	259
6106	cold	12	0	M	241
6118	cold	12	1	I	240
6120	cold	12	1	I	248
6126	cold	12	1	I	201
6191	cold	12	0	I	227
6148	cold	12	1	I	213
6149	cold	12	1	I	226
6151	cold	12	1	I	243
6242	warm	12	0	M	377
6249	warm	12	1	I	279
6250	warm	12	1	I	294
6251	warm	12	1	I	376
6254	warm	12	0	I	296
6259	warm	12	0	M	281
6260	warm	12	0	M	374
6264	warm	12	0	I	268
6265	warm	12	0	I	282

Sample prep and extraction

I did everything the same as what I’ve done for the past two extractions (1 and 2). No obvious mishaps or mistakes as far as I know.

Results:

[Raw qubit] [Google sheet qubit]

qubit_tube_conc_ng.ml	original_sample_conc_ng.ul	sample_vol_ul	dilution_factor	tube_number	extraction_method	ul_sample-used	elution_vol_ul	total-yield_ng
244	24.4	2	100	282	Zymo_microprep	35	15	317.2
152	15.2	2	100	268	Zymo_microprep	35	15	197.6
183	18.3	2	100	374	Zymo_microprep	35	15	237.9
231	23.1	2	100	281	Zymo_microprep	35	15	300.3
84.9	8.49	2	100	296	Zymo_microprep	35	15	110.37
84.7	8.47	2	100	376	Zymo_microprep	35	15	110.11
52.3	5.23	2	100	294	Zymo_microprep	35	15	67.99
355	35.5	2	100	279	Zymo_microprep	35	15	461.5
223	22.3	2	100	377	Zymo_microprep	35	15	289.9
23	2.3	2	100	243	Zymo_microprep	35	15	29.9
262	26.2	2	100	226	Zymo_microprep	35	15	340.6
340	34	2	100	213	Zymo_microprep	35	15	442
180	18	2	100	227	Zymo_microprep	35	15	234
62.7	6.27	2	100	201	Zymo_microprep	35	15	81.51
134	13.4	2	100	248	Zymo_microprep	35	15	174.2
216	21.6	2	100	240	Zymo_microprep	35	15	280.8
130	13	2	100	241	Zymo_microprep	35	15	169
317	31.7	2	100	259	Zymo_microprep	35	15	412.1
273	27.3	2	100	310	Zymo_microprep	35	15	354.9
178	17.8	2	100	315	Zymo_microprep	35	15	231.4
29	2.9	2	100	329	Zymo_microprep	35	15	37.7
274	27.4	2	100	303	Zymo_microprep	35	15	356.2
311	31.1	2	100	325	Zymo_microprep	35	15	404.3
289	28.9	2	100	301	Zymo_microprep	35	15	375.7

Summary table of what I have extracted so far:

from Grace’s Lab Notebook https://ift.tt/2q3zf4h
via IFTTT