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Costs assosiated with DNA sequencing since 2001

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Files Size Format Created Updated License Source
2 39kB csv zip 6 years ago 6 years ago ODC-PDDL-1.0 Sequencing Cost Table
DNA Sequencing Costs Data Description > 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 read more
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Data Files

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)

sequencing-costs  

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This is a preview version. There might be more data in the original version.

Field information

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

Integrate this dataset into your favourite tool

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)
    }
  }
})()

Read me

DNA Sequencing Costs

Data

Description

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.

Temporal coverage

2001-2015

Further Information

For further information regarding cost categories, DNA Sequencing Technologies, Quality and Genome Coverage please visit: http://www.genome.gov/sequencingcosts/

Citations

  1. Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Available at: www.genome.gov/sequencingcosts. Accessed [21-12-2015].

Sources

Preparation

Requirements

Python 2 together with modules urllib and datautil are required in order to process the data.

Processing

Run the following script from this directory to download and process the data:

make

Resources

  • The raw data are stored on directory ./archive/.
  • The processed data are stored on directory ./data.

License

ODC-PDDL-1.0

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/

Datapackage.json

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