langerhans

Updating MutliDoge checkpoints

Why?

If you are using MultiDoge and are stuck far behind in syncing, then this guide is for you. Even if you are currently already synced it's worth following this guide to be prepared for future re-syncs, but you can skip some steps. These new checkpoints contain blocks up to Jan 30th 2021. If you are trying to sync a wallet that has been created before around summer 2016, then sadly this won't really help you.

How?

1. Close MultiDoge completely.
2. Download this file: multidoge.checkpoints
3. Copy this file to the MultiDoge data directory. See below for the path. Overwrite the existing file. If you are already synced, you're done at this point, If not, continue.
4. Start MultiDoge.

jivanpal

#!/bin/bash

if [ "$1" = "-s" ] || [ "$1" = "--show" ]; then
    show=true
else
    show=false
fi

if $show || [ "$1" = "-v" ] || [ "$1" = "--verbose" ]; then
    verbose=true

sirselim

Jetson Xavier basecalling notes

initial basecalling runs

'fast' flip-flop calling on the Jetson Xavier

guppy_basecaller --disable_pings --compress_fastq -c dna_r9.4.1_450bps_fast.cfg -i flongle_fast5_pass/ -s flongle_test2 -x 'auto' --recursive 

bourque

rocarvaj

#!/usr/bin/perl
use strict;
use warnings;

=head1 NAME

rename - renames multiple files

=head1 SYNOPSIS

darencard

Useful Genomics Oneliners

The following commands sometimes require non-standard software like bioawk and seqtk.

rename scaffold headers with sequential numbers and lengths ("scaffold-N ")

bioawk -c fastx '{ print ">scaffold-" ++i" "length($seq)"\n"$seq }' < genome.fasta > new_genome.fasta

make association table of old and renamed scaffold names after above renaming command

lyndametref

R Cheat Sheet : Applying functions

• R Cheat Sheet : Applying functions
  • apply(x,index,function)
  • lapply(x,function)
  • sapply(x,function)
  • tapply(x,y,function)
  • mapply(function,x,y,...)
  • References

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

slowkow

#' Convert counts to transcripts per million (TPM).
#' 
#' Convert a numeric matrix of features (rows) and conditions (columns) with
#' raw feature counts to transcripts per million.
#' 
#'    Lior Pachter. Models for transcript quantification from RNA-Seq.
#'    arXiv:1104.3889v2 
#'    
#'    Wagner, et al. Measurement of mRNA abundance using RNA-seq data:
#'    RPKM measure is inconsistent among samples. Theory Biosci. 24 July 2012.

marcelm

from pysam import AlignmentFile
from pyfaidx import Fasta

def has_mismatch_in_interval(reference, bamfile, chrom, start, end):
    """
    Return whether there is a mismatch in the interval (start, end) in any read mapping to the given chromosome.

    reference -- a pyfaidx.Fasta object or something that behaves similarly
    """
    for column in bamfile.pileup(chrom, start, end):