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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 39kB | csv zip | 6 years ago | 5 years ago | ODC-PDDL-1.0 | Sequencing Cost Table |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
sequencing-costs | 2kB | csv (2kB) , json (4kB) | ||
genome-sequencing-costs_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 5kB | zip (5kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Date | 1 | date (%Y-%m-%d) | Date format YYYY-MM |
Cost per Mb | 2 | number | The cost of determining one megabase (Mb; a million bases) of DNA sequence of a specified quality |
Cost per Genome | 3 | number | The cost of sequencing a human-sized genome |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/genome-sequencing-costs
data info core/genome-sequencing-costs
tree core/genome-sequencing-costs
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/genome-sequencing-costs/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/genome-sequencing-costs/r/0.csv
curl -L https://datahub.io/core/genome-sequencing-costs/r/1.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/core/genome-sequencing-costs/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
Note: You might need to run the script with root permissions if you are running on Linux machine
Install the Frictionless Data data package library and the pandas itself:
pip install datapackage
pip install pandas
Now you can use the datapackage in the Pandas:
import datapackage
import pandas as pd
data_url = 'https://datahub.io/core/genome-sequencing-costs/datapackage.json'
# to load Data Package into storage
package = datapackage.Package(data_url)
# to load only tabular data
resources = package.resources
for resource in resources:
if resource.tabular:
data = pd.read_csv(resource.descriptor['path'])
print (data)
For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):
pip install datapackage
To get Data Package into your Python environment, run following code:
from datapackage import Package
package = Package('https://datahub.io/core/genome-sequencing-costs/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
If you are using JavaScript, please, follow instructions below:
Install data.js
module using npm
:
$ npm install data.js
Once the package is installed, use the following code snippet:
const {Dataset} = require('data.js')
const path = 'https://datahub.io/core/genome-sequencing-costs/datapackage.json'
// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
const dataset = await Dataset.load(path)
// get list of all resources:
for (const id in dataset.resources) {
console.log(dataset.resources[id]._descriptor.name)
}
// get all tabular data(if exists any)
for (const id in dataset.resources) {
if (dataset.resources[id]._descriptor.format === "csv") {
const file = dataset.resources[id]
// Get a raw stream
const stream = await file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()
DNA Sequencing Costs
For many years, the National Human Genome Research Institute (NHGRI) has tracked the costs associated with DNA sequencing performed at the sequencing centers funded by the Institute. This information has served as an important benchmark for assessing improvements in DNA sequencing technologies and for establishing the DNA sequencing capacity of the NHGRI Genome Sequencing Program (GSP). Here, NHGRI provides an analysis of these data, which gives one view of the remarkable improvements in DNA sequencing technologies and data-production pipelines in recent years.
2001-2015
For further information regarding cost categories, DNA Sequencing Technologies, Quality and Genome Coverage please visit: http://www.genome.gov/sequencingcosts/
Python 2 together with modules urllib and datautil are required in order to process the data.
Run the following script from this directory to download and process the data:
make
./archive/
../data
.This Data Package is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
Customized data (e.g. you need different or additional data)
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