--- tags: BRAILLE title: BRAILLE update (20-May-2021) --- [toc] --- >**Interactive function plots and tables are [here](https://astrobiomike.shinyapps.io/braille-mg/)**. --- # 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>