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:

Calculations were performed using Python in Jupyter Notebooks.

Genome Features Jupyter Notebook (GitHub):

Repeat Features Jupyter Notbooke (GitHub):

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:

Validate DMR bed files in IGV

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