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tags: BRAILLE
title: BRAILLE update (20-May-2021)
---
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>**Interactive function plots and tables are [here](https://astrobiomike.shinyapps.io/braille-mg/)**.
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# BRAILLE update (20-May-2021)
> Previous update docs
> - [https://hackmd.io/@astrobiomike/BRAILLE-notes-28-Apr-2021](https://hackmd.io/@astrobiomike/BRAILLE-notes-28-Apr-2021)
> - [https://hackmd.io/@astrobiomike/BRAILLE-notes-31-Mar-2021](https://hackmd.io/@astrobiomike/BRAILLE-notes-31-Mar-2021)
> - [https://hackmd.io/@astrobiomike/BRAILLE-notes-17-Mar-2021](https://hackmd.io/@astrobiomike/BRAILLE-notes-17-Mar-2021)
> - [https://hackmd.io/@astrobiomike/BRAILLE-update-24-Feb-2021](https://hackmd.io/@astrobiomike/BRAILLE-update-24-Feb-2021)
> - [https://hackmd.io/@astrobiomike/BRAILLE-update-3-Feb-2021](https://hackmd.io/@astrobiomike/BRAILLE-update-3-Feb-2021)
> - [https://hackmd.io/@astrobiomike/BRAILLE-notes-12-Dec-2020](https://hackmd.io/@astrobiomike/BRAILLE-notes-12-Dec-2020)
Metagenomic processing repository with data and code is at [OSF here](https://osf.io/uhk48/wiki/home/). All data are there, but not necessarily all of the processing and figures in these note docs.
# Gathering other metagenomics datasets
Metagenomes (all illumina paired-end fortunately)
* have
* 1 permafrost (sediment) ERR2752925
* 1 acidic water (water) ERR2752922
* 1 sulphur spring (water) ERR2752920
* 1 acidic/brine lake (sediment) SRR1574693
* 1 terrestrial subsurface aquifer (water) SRR6186653
* 2 seafloor subsurface (water) SRR3732688, SRR3723048
* 1 hypersaline mat (mat) (from us; not published yet; er maybe it is with the fungi paper...)
* 3 lava tube from Diana Hawaii (not sure)
* 10 braille lava tubes (film/ooze/substrates)
* trying to get
* 2 from terrestrial subsurface mine (water) wrote Lily
* 3 from terrestrial subsurface rocks (rocks) wrote authors
## Notes
```
papers:
Microbial Life in the Deep Subsurface Aquifer Illuminated by Metagenomics:
https://www.frontiersin.org/articles/10.3389/fmicb.2020.572252/full
WANT * metagenome5P SRX3296696 | SRR6186653 | borehole, Russia, 2.8 km deep
Metagenome sequencing and 98 microbial genomes from Juan de Fuca Ridge flank subsurface fluids
https://www.nature.com/articles/sdata201737
PRJNA269163
2 metagenomes from juan de fuca subsurface fluids
WANT * 1362B_J2.571 SRX1888493 | SRR3732688 | juan de fuca subsurface fluid
WANT * 1362A_J2.573 SRX1880758 | SRR3723048 | juan de fuca subsurface fluid
Taxonomic and functional analyses of intact microbial communities thriving in extreme, astrobiology-relevant, anoxic sites
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-020-00989-5
https://www.ncbi.nlm.nih.gov/sra/?term=PRJEB28336
WANT * permafrost_6SOB ERX2765971 | ERR2752925 | Yedoma_permafrost_DNA
WANT * Lake_Graenavatn_3Ice ERX2765968 | ERR2752922 | acidic_water_DNA
WANT * Sippenauer_Moor_1Sipp ERX2765966 | ERR2752920 | sulphur_spring_DNA
DONT WANT * permafrost_5SOB ERX2765970 | ERR2752924 | Yedoma_permafrost_PMA
DONT WANT * Lake_Graenavatn_4Ice ERX2765969 | ERR2752923 | acidic_water_PMA
DONT WANT * Sippenauer_Moor_2Sipp ERX2765967 | ERR2752921 | sulphur_spring_PMA
Exploration of deep terrestrial subsurface microbiome in Late Cretaceous Deccan traps and underlying Archean basement, India
https://www.nature.com/articles/s41598-018-35940-0
3 rock metagenomes (1 basalt; 1 granite; 1 transition)
No reads available, i wrote the corresponding author asking for them
Lily paper: fluids from deep terrestrial cave
SAMN06064269; IMG 3300007354 (SURF-B)
SAMN06064270; IMG 3300007352 (SURF-D)
# bah, can't find the reads, go figure... Emailed Lily
Insights from the Metagenome of an Acid Salt Lake: The Role of Biology in an Extreme Depositional Environment
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122869
WANT * YilgarnCraton SRX699729 | SRR1574693 | acidic brine lake sediment, australia
```
# FeGenie coverage view
Ironed out how to run FeGenie with our gene calls (to keep everything labeled the same) and still take into account gene neighborhoods (it was assigning a lot of false positives before - chatted a lot with Arkadiy about this). Wrote program to parse FeGenie output and our gene-coverage to be able to automate making the coverage view. Broadview didn't look all that different in the end, but still important to have.
## Unique gene copies
<a href="https://i.imgur.com/pWVmmWz.png"><img src="https://i.imgur.com/pWVmmWz.png"></a>
## Coverage
<a href="https://i.imgur.com/DXIx8hg.png"><img src="https://i.imgur.com/DXIx8hg.png"></a>
## Ratio of coverage / unique gene-copies
Not sure how much I want to read into this yet, but it's another window to look into.
<a href="https://i.imgur.com/uFpQaCw.png"><img src="https://i.imgur.com/uFpQaCw.png"></a>
## Heatmap of coverage
Scaled by Fe category (across rows) so that we can see smaller differences if they exist. Not yet arranged the same as above though unfortunately
<a href="https://i.imgur.com/ftawAIB.png"><img src="https://i.imgur.com/ftawAIB.png"></a>