| Chapter title |
Discovering sequence motifs.
|
|---|---|
| Chapter number | 12 |
| Book title |
Bioinformatics
|
| Published in |
Methods in molecular biology, January 2008
|
| DOI | 10.1007/978-1-60327-159-2_12 |
| Pubmed ID | |
| Book ISBNs |
978-1-58829-707-5, 978-1-60327-159-2
|
| Authors |
Timothy L. Bailey, Bailey, Timothy L. |
| Abstract |
Sequence motif discovery algorithms are an important part of the computational biologist's toolkit. The purpose of motif discovery is to discover patterns in biopolymer (nucleotide or protein) sequences in order to better understand the structure and function of the molecules the sequences represent. This chapter provides an overview of the use of sequence motif discovery in biology and a general guide to the use of motif discovery algorithms. The chapter discusses the types of biological features that DNA and protein motifs can represent and their usefulness. It also defines what sequence motifs are, how they are represented, and general techniques for discovering them. The primary focus is on one aspect of motif discovery: discovering motifs in a set of unaligned DNA or protein sequences. Also presented are steps useful for checking the biological validity and investigating the function of sequence motifs using methods such as motif scanning--searching for matches to motifs in a given sequence or a database of sequences. A discussion of some limitations of motif discovery concludes the chapter. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| United Kingdom | 3 | 2% |
| France | 2 | 2% |
| Sweden | 1 | <1% |
| Canada | 1 | <1% |
| Switzerland | 1 | <1% |
| China | 1 | <1% |
| Saudi Arabia | 1 | <1% |
| Unknown | 117 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 42 | 32% |
| Student > Ph. D. Student | 32 | 24% |
| Student > Master | 13 | 10% |
| Student > Bachelor | 12 | 9% |
| Professor | 5 | 4% |
| Other | 12 | 9% |
| Unknown | 15 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 66 | 50% |
| Biochemistry, Genetics and Molecular Biology | 21 | 16% |
| Computer Science | 14 | 11% |
| Environmental Science | 2 | 2% |
| Medicine and Dentistry | 2 | 2% |
| Other | 6 | 5% |
| Unknown | 20 | 15% |