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[Life Out of Sequence: A Data-Driven History of Bioinformatics] [Author: Stevens, Hallam] [November, 2013]
Thirty years ago, the most likely place to find a biologist was standing at a laboratory bench, peering down a microscope, surrounded by flasks of chemicals and petri dishes full of bacteria. Today, you are just as likely to find him or her in a room that looks more like an office, poring over lines of code on computer screens. The use of computers in biology has radically transformed who biologists are, what they do, and how they understand life. In Life Out of Sequence, Hallam Stevens looks inside this new landscape of digital scientific work. Stevens chronicles the emergence of bioinformatics—the mode of working across and between biology, computing, mathematics, and statistics—from the 1960s to the present, seeking to understand how knowledge about life is made in and through virtual spaces. He shows how scientific data moves from living organisms into DNA sequencing machines, through software, and into databases, images, and scientific publications. What he reveals is a biology very different from the one of predigital a biology that includes not only biologists but also highly interdisciplinary teams of managers and workers; a biology that is more centered on DNA sequencing, but one that understands sequence in terms of dynamic cascades and highly interconnected networks. Life Out of Sequence thus offers the computational biology community welcome context for their own work while also giving the public a frontline perspective of what is going on this rapidly changing field.
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First published January 1, 2013
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About the author
Hallam Stevens
6 books2 followersHallam Stevens is Assistant Professor of History in the School of Humanities and Social Sciences at Nanyang Technological University (Singapore). He is the author of Life Out of Sequence: A Data-Driven History of Bioinformatics.
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Displaying 1 - 4 of 4 reviews
December 31, 2017
A great read for this computer guy.
How did bioinformatics get to be the engine that drives genomics?
A great overview of the intersection of computing and biology for the non-biology major.
How did bioinformatics get to be the engine that drives genomics?
A great overview of the intersection of computing and biology for the non-biology major.
March 29, 2020
Upon reading the title of this book, many non-specialists might rightly ask, “What is bioinformatics? And why does it deserve its own history?” For the first question, bioinformatics is the application of computer technology to biological studies, and I hope that reading this review will answer the second question.
Many of us were taught hypothesis-driven biology in school – that is, we were taught to ask a well-formed question, perform an experiment, and confirm/deny the hypothesis. While such research will still have its place, a newer form of inquiry has grown up in recent years. In this form, we correlate genetic information with external evidence to solve problems. The sequencing of the human genome in the Human Genome Project stands as the seminal event that produced this new field.
As I write, I wear a t-shirt with the name of my employer (a major medical research center) on it. It talks about forming the future of personalized medicine. Such medicine is dictated by risk factors for various conditions based individually upon a patient’s genome. It hopes to be the future of medical care. It is driven by statistical analyses and integrated by computer technology.
This book, written by a historian of science, shares how this new approach to knowledge has taken place. It integrates expert-level scientific knowledge from computer science and biology into a coherent historical narrative. It informs us about our past so that we may more confidently approach our future. It is well written and based in no small part upon the author’s personal experiences at a lab at MIT during his doctoral work.
Stevens takes us through phases of the development of this field. He starts with the advent of computer technology and traces its impact on bio-related fields. Then he talks about how it organizes the space of genetics and thus produces massive amounts of data. These data then reorient how we approach problems through modern programming techniques. These allow us to approach the problem of how to understand our own DNA meaningfully. Like all good histories of science, this work manages the confluence of many founts of knowledge into one working stream of social impact.
Though dated (written in 2013), this work is of acute interest for those of us who work at the intersection of computer science and biology/medicine. However, this small niche of knowledge is increasing in importance for the general public. Genetic analysis, with each year, is ineluctably entering the mainstream of modern society. Stevens’ history tells us in detail what this transformation consists of and predicts how it will take place. Those aware of societal shifts should take notice.
Many of us were taught hypothesis-driven biology in school – that is, we were taught to ask a well-formed question, perform an experiment, and confirm/deny the hypothesis. While such research will still have its place, a newer form of inquiry has grown up in recent years. In this form, we correlate genetic information with external evidence to solve problems. The sequencing of the human genome in the Human Genome Project stands as the seminal event that produced this new field.
As I write, I wear a t-shirt with the name of my employer (a major medical research center) on it. It talks about forming the future of personalized medicine. Such medicine is dictated by risk factors for various conditions based individually upon a patient’s genome. It hopes to be the future of medical care. It is driven by statistical analyses and integrated by computer technology.
This book, written by a historian of science, shares how this new approach to knowledge has taken place. It integrates expert-level scientific knowledge from computer science and biology into a coherent historical narrative. It informs us about our past so that we may more confidently approach our future. It is well written and based in no small part upon the author’s personal experiences at a lab at MIT during his doctoral work.
Stevens takes us through phases of the development of this field. He starts with the advent of computer technology and traces its impact on bio-related fields. Then he talks about how it organizes the space of genetics and thus produces massive amounts of data. These data then reorient how we approach problems through modern programming techniques. These allow us to approach the problem of how to understand our own DNA meaningfully. Like all good histories of science, this work manages the confluence of many founts of knowledge into one working stream of social impact.
Though dated (written in 2013), this work is of acute interest for those of us who work at the intersection of computer science and biology/medicine. However, this small niche of knowledge is increasing in importance for the general public. Genetic analysis, with each year, is ineluctably entering the mainstream of modern society. Stevens’ history tells us in detail what this transformation consists of and predicts how it will take place. Those aware of societal shifts should take notice.
April 28, 2022
While the buzz phrases "big data" and "data-driven x" are relatively new and are pushed by various actors as ground-breaking and exciting, science has always been data-driven. What's new is our expanded ability to process and find patterns in vast data sets with help from powerful computers. We still tend to visualize biologists and chemists as wearing white lab-coats, surrounded by chemicals and petri dishes, in a wet lab, whereas, today, scientists in nearly all disciplines spend a considerable portion of their time in a dry lab, facing computer displays and dealing with computational analyses, modeling, or simulations.
Bioinformatics, a discipline at the intersection of biology and computing is concerned with the acquisition, storage, analysis, and dissemination of biological data, most often DNA and amino-acid sequences. The word "bioinformatics" is derived from "biology" and "informatics," which, along with the French form "informatique," is the preferred word for "computing" or "computer science" in Europe. "Bioinformatics" is nicer-sounding than "biocomputing"; furthermore, the latter term has come to signify the design and use of computing devices built from biological components.
In a broader sense, bioinformatics is the study of information content and information flow in biological systems and processes. Even though bioinformatics (aka computational molecular biology) emerged in the 1960s with the efforts of Margaret O. Dayhoff, Walter M. Fitch, Russell F. Doolittle, and others, it came into prominence in the 21s century, when, on the heels of successes in the sequencing of genomes for simple organisms, the sequencing of the human genome became feasible.
In Life out of Sequence, Stevens draws from his own field work, interviews, and published research to explore the dynamic relationship between biology and computing, that is, the manner in which biology shapes and is shaped by digital technologies. Stevens's highly-accessible account informs us of the ways in which computers influence the organization of research in biology and how they assist with data collection and knowledge production. The role of data in biological research is far from a one-way journey from the lab to the computer. Data also plays a key role in shaping the experiments.
Bioinformatics, a discipline at the intersection of biology and computing is concerned with the acquisition, storage, analysis, and dissemination of biological data, most often DNA and amino-acid sequences. The word "bioinformatics" is derived from "biology" and "informatics," which, along with the French form "informatique," is the preferred word for "computing" or "computer science" in Europe. "Bioinformatics" is nicer-sounding than "biocomputing"; furthermore, the latter term has come to signify the design and use of computing devices built from biological components.
In a broader sense, bioinformatics is the study of information content and information flow in biological systems and processes. Even though bioinformatics (aka computational molecular biology) emerged in the 1960s with the efforts of Margaret O. Dayhoff, Walter M. Fitch, Russell F. Doolittle, and others, it came into prominence in the 21s century, when, on the heels of successes in the sequencing of genomes for simple organisms, the sequencing of the human genome became feasible.
In Life out of Sequence, Stevens draws from his own field work, interviews, and published research to explore the dynamic relationship between biology and computing, that is, the manner in which biology shapes and is shaped by digital technologies. Stevens's highly-accessible account informs us of the ways in which computers influence the organization of research in biology and how they assist with data collection and knowledge production. The role of data in biological research is far from a one-way journey from the lab to the computer. Data also plays a key role in shaping the experiments.
August 29, 2017
Next-generation sequencing may be “bioinformatic” not just because it produces huge amounts of data, and not just because it brings material and virtual biology closer together, but also because it helps to generate just the kinds of new relationships and patterns that we have seen in bioinformatic software, databases, and visualizations. In particular, next-generation sequencing reminds us that biology is increasingly driven by the exigencies of data. It is not just that biology is becoming information, but rather that biology is coming to depend more and more on the technologies and information structures that store and move data.
A history of bioinformatics can thus only be narrated alongside histories of the Internet and information technology.
Bioinformatics and the Internet continue to face similar problems and continue to develop in parallel. In particular, the futures envisioned for the Internet remain deeply intertwined with efforts to understand biology, and especially the human genome.
As biology and the web share both a history and a set of problems, understanding the future of biology requires understanding where the Internet is headed. Nowadays the most intellectually satisfying and promising work can be found at the intersection of deep learning, genomics, and the blockchain.
A history of bioinformatics can thus only be narrated alongside histories of the Internet and information technology.
Bioinformatics and the Internet continue to face similar problems and continue to develop in parallel. In particular, the futures envisioned for the Internet remain deeply intertwined with efforts to understand biology, and especially the human genome.
As biology and the web share both a history and a set of problems, understanding the future of biology requires understanding where the Internet is headed. Nowadays the most intellectually satisfying and promising work can be found at the intersection of deep learning, genomics, and the blockchain.
Displaying 1 - 4 of 4 reviews