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#Read a hist file from bedtools coverage -hist and create a coverage per orf file.
#Coverage per each gene is calculated as: coverage_orfA = sum(depth_of_coverage * fraction_covered)
{
    if ($0 i ~ /^all/){
        next;
    }else{
        split($4,a,"_");
        b=$1"_"$6"_"$2+1"_"$3"_orf-"a[2]"\t"$13;
        c[b]=c[b] + ($11*$14)
    }

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#!/bin/bash

# We need one fasta file export with the aligned sequences and another with the SS SAI filter
ALIGN=${1}
OFILE=${1/.fasta/.stk}

# Creating the first part of the file
# we need to linearize the fasta file
awk '
BEGIN{

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515F  GTGCCAGCMGCCGCGGTAA
515F-Y  GTGYCAGCMGCCGCGGTAA
515YF GTGYCAGCMGCCGCGGTAA
341F  CCTACGGGNGGCWGCAG
U341F CCTAYGGGRBGCASCAG
Eu565F  CCAGCASCYGCGGTAATTCC
B806R GGACTACNVGGGTWTCTAAT
806R  GGACTACHVGGGTWTCTAAT
926R  CCGYCAATTYMTTTRAGTTT
906R-jed  CCGYCAATTYMTTTRAGTTT

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#' Source the R code from an knitr file, optionally skipping plots
#'
#' @param file the knitr file to source
#' @param skip_plots whether to make plots. If TRUE (default) sets a null graphics device
#'
#' @return This function is called for its side effects
#' @export
source_rmd = function(file, skip_plots = TRUE) {
  temp = tempfile(fileext=".R")
  knitr::purl(file, output=temp)

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# Example for N. maritimus
# First we generate a set of mutants
python generate_mutants.py -g GCF_000018465.1_ASM1846v1_protein.faa -n 3 -o nmarit

# We label our seeds or references
awk '{if ($0 ~ /^>/) {print $1"_ref"}else{print $0}}' ../GCF_000018465.1_ASM1846v1_protein.faa > nmarit_aa_ref.fasta

# Concatenate mutants and references
cat nmar*sub.fasta nmar*ins.fasta nmar*del.fasta nmar*all.fasta nmarit_aa_ref.fasta > nmarit_all_aa.fasta

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#' ## Storing R Objects in a SQLite Database

#' Two packages we are using. The first is the ```RSQLite``` which will be used to create and manage an in-memory SQLite database. The second is ```igraph``` which I will use to create and visualize a random network. Some of the work I do is on network simulation. I often don't know the metrics I need from a simulated network when it's created, so I want to be able to store the networks that are created so that I can go back later and analyze them.
library(RSQLite)
library(igraph)

#' Create a database in memory.
con <- dbConnect(SQLite(), ":memory:")

#' The table has two columns, an *id* column and a column called *graph* which is a **blob** type. This type just stores binary data.

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# UCLUST workflow ---------------------------------------------------------
library(tidyverse)
library(cowplot)
library(ggpubr)

setwd("~/Downloads/alma_original_test")

uclust_df <- read_tsv("16s.insilico_mock.tsv", col_names = T)

uclust_16S_summary <- uclust_df%>% dplyr::rename(otu_name = `#OTU ID`) %>%

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big_dada <- function(X){
cat("Processing:", X, "\n")
cat("Derep F:", X, "\n")
derepF <- derepFastq(filtpathF[[X]])
cat("Inferring F:", X, "\n")
ddF <- dada(derepF, err=errF, multithread=TRUE)
cat("Derep R:", X, "\n")
derepR <- derepFastq(filtpathR[[X]])
cat("Inferring R:", X, "\n")
ddR <- dada(derepR, err=errR, multithread=TRUE)

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mmseqs concatdbs /PROCESSING/OSD/UNKNOWNS/MMSEQ_MPI/unkdb_update_hmp/unkprot_db \
  /PROCESSING/OSD/UNKNOWNS/MMSEQ_MPI/unkdb_update_hmp/HMPSOAP_db \
  /PROCESSING/OSD/UNKNOWNS/MMSEQ_MPI/unkdb_update_hmp/unkprot_db.withNewSequences

mmseqs concatdbs /PROCESSING/OSD/UNKNOWNS/MMSEQ_MPI/unkdb_update_hmp/unkprot_db_h \
  /PROCESSING/OSD/UNKNOWNS/MMSEQ_MPI/unkdb_update_hmp/HMPSOAP_db_h \
  /PROCESSING/OSD/UNKNOWNS/MMSEQ_MPI/unkdb_update_hmp/unkprot_db.withNewSequences_h

mawk '{print NR"\t"$1}' unkprot_db.withNewSequences_h | sed 's/\x0//g' > unkprot_db.withNewSequences.lookup

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#!/bin/bash -l
#$ -cwd
#$ -j y
#$ -t 1-10:1
#$ -tc 25
#$ -N hmm_unk
#$ -pe threaded 2
#$ -V

# Where the models are