Learn to use Entrez the main search engine for the National Center for Biotechnology Information, NCBI. An understanding of the features of Entrez will improve all of your searches at the NCBI site--whether you seek genes, genomes, proteins, literature, or any of the other extensive data collections at NCBI. This is because Entrez provides a variety of tools for searching and retrieving that are common to many of the NCBI databases. Here we will review and highlight the general features of many of the most important Entrez tools.

You will learn:

  • how to globally search all Entrez databases at once
  • to perform basic, advanced, and Boolean searches effectively
  • to interpret the results lists and understand the displays
  • about some of the more advanced Entrez tools, such as Batch Entrez and Batch Citation Matcher
  • strategies to customize and save searches to re-run later, and output mechanisms for results
TUTORIAL RELATED CONTENT

TUTORIALS

This tutorial is a part of the tutorial group Text-related tools. You might find the other tutorials in the group interesting:

PubMatrix: PubMatrix, an on-line tool for multiplex literature mining of the PubMed database.

iHOP: Information Hyperlinked Over Proteins text mining resource

STRING: known and predicted protein-protein interactions

Textpresso: Text-mining the biological literature

Gene Ontology: Gene Ontology controlled vocabularies in biology

XplorMed: eXploring Medline abstracts

GoMiner: Ascribe biological significance to large lists of genes by annotating them with their corresponding GO categories

Controlled Vocabularies: Standardized term lists that can enhance interactions with biological databases

DAVID: A tool that analyzes large lists of genes to provide biological meaning

PubMed: PubMed access to biomedical research literature

CATEGORIES

Miscellaneous : Broad overview training suites, or those not categorized by other existing collections, may be located here.

NCBI : This category includes resources maintained at the National Center for Biotechnology Information (NCBI).

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BIOMED CENTRAL

Recent BioMed Central research articles citing this resource

Tseytlin Eugene et al., NOBLE – Flexible concept recognition for large-scale biomedical natural language processing Knowledge-based analysis. BMC Bioinformatics (2016) doi:10.1186/s12859-015-0871-y

Hakenberg Jörg et al., Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts Sequence analysis (applications). BMC Bioinformatics (2016) doi:10.1186/s12859-015-0865-9

Bourgeois Stephane et al., A multi-factorial analysis of response to warfarin in a UK prospective cohort. Genome Medicine (2016) doi:10.1186/s13073-015-0255-y

Chen Yuan et al., Informative gene selection and the direct classification of tumors based on relative simplicity Results and data. BMC Bioinformatics (2016) doi:10.1186/s12859-016-0893-0

Yu Hasun et al., Prediction of drugs having opposite effects on disease genes in a directed network. BMC Systems Biology (2016) doi:10.1186/s12918-015-0243-2