Question: Why is knowledge of the DNA sequence important in research?
About the article "Nucleic acid sequence", section "Sequence determination".

Candidate passages:
DNA sequencing is the process of determining the nucleotide sequence of a given DNA fragment.
The sequence of the DNA of a living thing encodes the necessary information for that living thing to survive and reproduce.
Therefore, determining the sequence is useful in fundamental research into why and how organisms live, as well as in applied subjects.
Because of the importance of DNA to living things, knowledge of a DNA sequence may be useful in practically any biological research.
For example, in medicine it can be used to identify, diagnose and potentially develop treatments for genetic diseases.
Similarly, research into pathogens may lead to treatments for contagious diseases.
Biotechnology is a burgeoning discipline, with the potential for many useful products and services.RNA is not sequenced directly.
Instead, it is copied to a DNA by reverse transcriptase, and this DNA is then sequenced.
Current sequencing methods rely on the discriminatory ability of DNA polymerases, and therefore can only distinguish four bases.
An inosine (created from adenosine during RNA editing) is read as a G, and 5-methyl-cytosine (created from cytosine by DNA methylation) is read as a C. With current technology, it is difficult to sequence small amounts of DNA, as the signal is too weak to measure.
This is overcome by polymerase chain reaction (PCR) amplification.
DNA sequencing is the process of determining the nucleotide order of a given DNA fragment.
So far, most DNA sequencing has been performed using the chain termination method developed by Frederick Sanger.
This technique uses sequence-specific termination of a DNA synthesis reaction using modified nucleotide substrates.
However, new sequencing technologies such as Pyrosequencing are gaining an increasing share of the sequencing market.
More genome data is now being produced by pyrosequencing than Sanger DNA sequencing.
Pyrosequencing has enabled rapid genome sequencing.
Bacterial genomes can be sequenced in a single run with several X coverage with this technique.
This technique was also used to sequence the genome of James Watson recently.
The sequence of DNA encodes the necessary information for living things to survive and reproduce.
Determining the sequence is therefore useful in fundamental research into why and how organisms live, as well as in applied subjects.
Because of the key nature of DNA to living things, knowledge of DNA sequence may come in useful in practically any biological research.
For example, in medicine it can be used to identify, diagnose and potentially develop treatments for genetic diseases.
Similarly, research into pathogens may lead to treatments for contagious diseases.
Biotechnology is a burgeoning discipline, with the potential for many useful products and services.
The Carlson Curve is a term coined by The Economist to describe the biotechnological equivalent of Moore's Law, and is named after author Rob Carlson.
Carlson accurately predicted that the doubling time of DNA sequencing technologies (measured by cost and performance) would be at least as fast as Moore's Law.
Carlson Curves illustrate the rapid (in some cases hyperexponential) decreases in cost, and increases in performance, of a variety of technologies, including DNA sequencing, DNA synthesis and a range of physical and computational tools used in protein expression and in determining protein structures. 
All the functions of DNA depend on interactions with proteins.
These protein interactions can be non-specific, or the protein can bind specifically to a single DNA sequence.
Enzymes can also bind to DNA and of these, the polymerases that copy the DNA base sequence in transcription and DNA replication are particularly important. 
Structural and physical properties of DNA provide important constraints on the binding sites formed on surfaces of DNA-binding proteins.
Characteristics of such binding sites may be used for predicting DNA-binding sites from the structural and even sequence properties of unbound proteins.
This approach has been successfully implemented for predicting the protein–protein interface.
Here, this approach is adopted for predicting DNA-binding sites in DNA-binding proteins.
First attempt to use sequence and evolutinary features to predict DNA-binding sites in proteins was made by Ahmad et al.
(2004) and Ahmad and Sarai (2005).
Some methods use structural information to predict DNA-binding sites and therefore require a three-dimensional structure of the protein, while others use only sequence information and do not require protein structure in order to make a prediction.
Structure- and sequence-based prediction of DNA-binding sites in DNA-binding proteins can be performed on several web servers listed below: 1) DISIS predicts DNA binding sites directly from amino acid sequence and hence is applicable for all known proteins.
It is based on the chemical-physical properties of the residue and its environment, predicted structural features and evolutionary data.
It uses machine learning algorithms.2) DISIS2 receives the raw amino acid sequence and generates all features from it, such as secondary structure, solvent accessibility, disorder, b-value, protein-protein interaction, coiled coils, and evolutionary profiles, etc.
The amount of predicted features is much larger than of DISIS (previous version).
Finally, DISIS2 is able to predict DNA-binding residues from protein sequence of DNA-binding proteins.3) DNABindR predicts DNA binding sites from amino acid sequences using machine learning algorithms.4) DISPLAR makes a prediction based on properties of protein structure.
Knowledge of the protein structure is required 5) BindN makes a prediction based on chemical properties of the input protein sequence.
Knowledge of the protein structure is not required.
6) BindN+, an upgraded version of BindN, applies support vector machines (SVMs) to sequence-based prediction of DNA or RNA-binding residues from biochemical features and evolutionary information.7) DP-Bind combines multiple methods to make a consensus prediction based on the profile of evolutionary conservation and properties of the input protein sequence.
Profile of evolutionary conservation is automatically generated by the web-server.
Knowledge of the protein structure is not required.8) DBS-PSSM (This article also shows how prediction can be significantly sped up by generating alignments against limited data sets).9) DBS-Pred (This article also uses amino acid composition analysis to predict DNA-binding proteins, and uses structure information to improve binding site prediction.
The method is based on single sequences only and thousands of proteins can be processed in less than an hour).
Standalone is also available. 
Breslauer determined a DNA database that allows one to energetically map genome sequence information so as to correlate regions of differential stabilities with regions of differential biological functions.
The database and its extensions also have been used to design probes and drugs with predictable hybridization and binding properties, a capability key to various diagnostic and therapeutic protocols.
Breslauer has mapped the energetic consequences of DNA damage, knowledge critical to understanding the mechanisms of DNA repair, including why certain mutations escape repair and result in cancer.
Most recently, Breslauer has elucidated the significance of metastable states within the rough energy landscapes of DNA molecules, information critical to our understanding of the molecular origins of triple repeat diseases, such as Huntington's disease and fragile X syndrome.<br>Breslauer is the Executive Editor of Biopolymers, a major journal in his field, and is founding editor of Nucleic Acid Sciences.
He also serves on numerous scientific advisory boards, including those associated with the Cancer Institute of New Jersey, the Center for Advanced Biotechnology and Medicine, as well as federal funding agency study sections.