elsherbini

from itertools import *

f = open("nucleotide_alignment.fa", "r")

ntseqs = [] # nucleotide sequences
ntgaps = [] # gaps in nucleotide sequences
ntnames = []
oldseq = ""
name = ""
coordinate = 0

elsherbini

Files in support of a statsexchange question:

https://stats.stackexchange.com/questions/441250/pope-effect-on-pizza-regression-with-presence-absence-and-similarity-data-as-d

Files

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

elsherbini

elsherbini

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,

elsherbini

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)

elsherbini

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

elsherbini

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)

1. forward reads
2. reverse reads

output:

elsherbini

>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

elsherbini

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

elsherbini