Back to Search
Start Over
Homology-Based Annotation of Large Protein Datasets
- Source :
- DATA MINING TECHNIQUES FOR THE LIFE SCIENCES, Carugo, O and Eisenhaber, F. DATA MINING TECHNIQUES FOR THE LIFE SCIENCES, 1415, HUMANA PRESS INC, pp.153-176, 2016, Methods in Molecular Biology, 978-1-4939-3572-7; 978-1-4939-3570-3. ⟨10.1007/978-1-4939-3572-7_8⟩, Methods in Molecular Biology ISBN: 9781493935703
- Publication Year :
- 2016
- Publisher :
- HAL CCSD, 2016.
-
Abstract
- International audience; Advances in DNA sequencing technologies have led to an increasing amount of protein sequence data being generated. Only a small fraction of this protein sequence data will have experimental annotation associated with them. Here, we describe a protocol for in silico homology-based annotation of large protein datasets that makes extensive use of manually curated collections of protein families. We focus on annotations provided by the Pfam database and suggest ways to identify family outliers and family variations. This protocol may be useful to people who are new to protein data analysis, or who are unfamiliar with the current computational tools that are available.
- Subjects :
- 0301 basic medicine
Protein family
Computer science
In silico
[SDV]Life Sciences [q-bio]
Sequence clustering
Computational biology
Protein family databases
Homology (biology)
DNA sequencing
Protein annotation
Homology
03 medical and health sciences
Annotation
030104 developmental biology
Protein sequencing
ComputingMethodologies_PATTERNRECOGNITION
Protein Annotation
Profile-hidden Markov models
Subjects
Details
- Language :
- English
- ISBN :
- 978-1-4939-3572-7
978-1-4939-3570-3 - ISBNs :
- 9781493935727 and 9781493935703
- Database :
- OpenAIRE
- Journal :
- DATA MINING TECHNIQUES FOR THE LIFE SCIENCES, Carugo, O and Eisenhaber, F. DATA MINING TECHNIQUES FOR THE LIFE SCIENCES, 1415, HUMANA PRESS INC, pp.153-176, 2016, Methods in Molecular Biology, 978-1-4939-3572-7; 978-1-4939-3570-3. ⟨10.1007/978-1-4939-3572-7_8⟩, Methods in Molecular Biology ISBN: 9781493935703
- Accession number :
- edsair.doi.dedup.....96dac742fc4f7d7382d06d36c74a4dbd
- Full Text :
- https://doi.org/10.1007/978-1-4939-3572-7_8⟩