| from itertools import * | |
| f = open("nucleotide_alignment.fa", "r") | |
| ntseqs = [] # nucleotide sequences | |
| ntgaps = [] # gaps in nucleotide sequences | |
| ntnames = [] | |
| oldseq = "" | |
| name = "" | |
| coordinate = 0 |
Files in support of a statsexchange question:
pope_pizza.Rmd - code to generate plots and simulate pizzas
pizzeria_coordinates_and_pope_preferences.csv - the locations of 20 pizzerias and which popes frequented them
pizzeria_menus.csv - presence/absence of different menu items in the 20 pizzerias
pizzeria_menu_item_similarities.csv - pairwise similarities of menu items at the different pizzerias
| country | year | emissions | |
|---|---|---|---|
| Brazil | 1996 | 1.7268642168 | |
| China | 1996 | 2.8443095815 | |
| India | 1996 | 0.901348777 | |
| Russian Federation | 1996 | 10.8866506666 | |
| United States | 1996 | 19.496024737 | |
| Brazil | 1997 | 1.7938286777 | |
| China | 1997 | 2.8205678906 | |
| India | 1997 | 0.9200723802 | |
| Russian Federation | 1997 | 10.3167892489 |
| library(tidyverse) | |
| library(cowplot) | |
| library(scales) | |
| library(colorblindr) | |
| point_map <- tribble(~place, ~old_value, ~new_value, | |
| 1,6,10, | |
| 2,5,7, | |
| 3,4,4, | |
| 4,3,2, |
| library(tidyverse) | |
| library(cowplot) | |
| dummy_data <- tibble(combo_id = seq_len(8), factor1=rep(c("1a","1b"), times=4), factor2=rep(rep(c("2a","2b"), each=2), times=2), factor3=rep(c("3a","3b"),each=4)) %>% | |
| {replicate(8, ., simplify = FALSE)} %>% | |
| bind_rows() %>% | |
| group_by(combo_id) %>% | |
| mutate(value=rpois(8, 2*combo_id)) | |
| head(dummy_data) |
| library(tidyverse) | |
| library(cowplot) | |
| library(colorblindr) | |
| library(ggbeeswarm) | |
| library(ggrepel) | |
| # human_usd from https://github.com/fdryan/R/blob/master/ggplot2_formatter.r copy and source that! | |
| df <- read_csv("nfl_salaries.csv") # from week 2 @ https://github.com/rfordatascience/tidytuesday |
A quick and somewhat untested pipeline to run cutadapt.
This will take paired reads corresponding to a single genome (already demultiplexed) and perform quality filtering and adapter trimming.
input (2 files)
output:
| >9CS106_NODE_2_length_702_cov_1339.861816_1 # 177 # 665 # -1 # ID=2_1;partial=00;start_type=ATG;rbs_motif=None;rbs_spacer=None;gc_cont=0.425 | |
| MDSLNNTDFKKLASQQKTIQMKMRLLALAHFKDGLSRTQIAKSLKVSRTSVNKWVRIFFE | |
| EGLEGLQEKPRTGRPAYLTDEQRAQLSAFIKKEAESPSGGRLVGSDIHDYIVKHFDKHYH | |
| PNSIYYLLDHMGFSWITSRSKHPKQSQQIQDDFKKIPNRNDP* | |
| >9CS106_NODE_3_length_68154_cov_55.956436_1 # 196 # 2079 # 1 # ID=3_1;partial=00;start_type=ATG;rbs_motif=None;rbs_spacer=None;gc_cont=0.471 | |
| MKLTLKQKLIGASLSAVVVMATALTWLSANQLFEQTRNGVYLRAESVSEAASEGIKNWID | |
| IRTDIASAFNDFSREDDVVPFLRQARVAGGFDDIFLGTPEGGMYRSHPERNRADYDPRQR | |
| PWYQEANAAGKQIITTAYQDAITKALLVTIAEPVRHNGQLVGVVGADVLIDQLVNDVISL | |
| DVGDNAYAMLIDASDGTFLAHPDSALSLKPVSQLSNDISMPIIENAVRTGSIEIIKERGA | |
| EKLLYFTKVPNTNWIFAVQMDKATEEANHSTLLTQLITTAVIITLIVIVLVSWLVSFLFR |
| import networkx as nx | |
| def setup_codon_graph(): | |
| """create a networkx graph of codons as nodes and mutations as codon_edges. | |
| if the mutation is synonymous, the edgeweight is 0.01 | |
| if the mutation is non-synonymous, the edgeweight is 1.01 | |
| then, by calculating the shortest path length between two bases, | |
| you can get the most parsimonious path |