Rob Patro rob-p

This is an interesting question, and I think the answer depends on what your primary goal is. Istvan makes good points in favor of straight-forward C (integrated with Python) for building tools that are easy for others, without a ton of experience in programming languages, to modify etc. However, if your primary goal is to make efficient and robust tools for others to use, then let me offer a somewhat different perspective.

The language in which one develops a project has important implications beyond just the speed and memory profiles that will result. Both C++ and Rust will allow you to write very fast tools, making use of zero-cost abstractions where feasible, with predictable and frugal memory profiles (assuming you choose the right algorithms and are careful in designing the programs). However, two important areas where I see these languages diverging are safety and maintainability. By safety, I mean minimizing the types of nefarious memory and correctness bugs that can easily slip into "bare-met

	from pafpy import PafFile
	import argparse
	import sys

	def parse_tab6(tf):
	res = []
	first = True
	with open(tf, 'r') as ifile:
	for l in ifile:
	if first:

	from pafpy import PafFile
	import argparse
	import sys

	def main(args):
	pi = args.input
	of = args.out

	ld = {}
	with open(args.lens, 'r') as ifile:

	from pafpy import PafFile
	import argparse
	import sys


	def main(argsj):
	pi = args.paf
	of = args.tblast

	def frac_covered(r):

	nextflow.enable.dsl=2

	process foo {
	publishDir "output"

	input:
	val(prefix)

	output:
	path "${prefix}_foo"

	from math import *

	# function [alpha, xmin, L]=plfit(x, varargin)
	# PLFIT fits a power-law distributional model to data.
	# Source: http://www.santafe.edu/~aaronc/powerlaws/
	#
	# PLFIT(x) estimates x_min and alpha according to the goodness-of-fit
	# based method described in Clauset, Shalizi, Newman (2007). x is a
	# vector of observations of some quantity to which we wish to fit the
	# power-law distribution p(x) ~ x^-alpha for x >= xmin.

	#!/usr/bin/env bash
	pn=$1
	convert $1 $1.ps
	gs -dPDFA -dBATCH -dNOPAUSE -dNOOUTERSAVE -dUseCIEColor -sProcessColorModel=DeviceCMYK -sDEVICE=pdfwrite -sPDFACompatibilityPolicy=1 -sOutputFile=$1.a.pdf $1.ps

	# image: COMBINE-lab/salmon
	# This dockerfile is based on the one created by
	# Titus Brown (available at https://github.com/ctb/2015-docker-building/blob/master/salmon/Dockerfile)
	FROM centos:latest

	ENV PACKAGES git gcc make g++ liblzma-dev libbz2-dev \
	ca-certificates zlib1g-dev libcurl4-openssl-dev curl unzip autoconf apt-transport-https ca-certificates gnupg software-properties-common wget
	ENV SALMON_VERSION 1.2.1

	# salmon binary will be installed in /home/salmon/bin/salmon

	/**
	MWE for salmon segfault
	**/

	#include <boost/thread/thread.hpp>

	#include <cstdint>
	#include <cstdio>
	#include <cstring>
	#include <fstream>

	#include "concurrentqueue.h"
	//#include "blockingconcurrentqueue.h"
	#include <thread>
	#include <atomic>
	#include <vector>
	#include <iostream>

	using namespace moodycamel ;
	using namespace std ;