Bioinformatics Database Search Engines: Entrez, DBGET, LinkDB, BLAST and FASTA

Quick Overview: Bioinformatics database search engines help researchers find biological information from very large collections of genes, proteins, genomes, structures, pathways and scientific records. Broadly, the search methods in this topic can be divided into text-based searching and sequence similarity-based searching.

In modern biology, enormous amounts of biological data are generated every day. Genome sequencing, transcriptomics, proteomics, structural biology and other high-throughput technologies continuously add new information to biological databases. Because these databases contain millions of records, simply knowing that a database exists is not enough. Researchers also need efficient tools to search, retrieve and compare the information stored in these databases.

This is where bioinformatics database search engines become important. A search engine can help a researcher find a gene using its name, retrieve a protein using an accession number, identify related database records, or compare an unknown DNA or protein sequence against known sequences.

The two major approaches covered in this topic are:

Text-based search engines: Entrez, DBGET and LinkDB
Sequence similarity-based search engines: BLAST and FASTA

The important point to remember is that these tools solve different types of problems. Entrez, DBGET and LinkDB mainly help us retrieve and connect information. BLAST and FASTA mainly help us compare biological sequences and identify similar sequences.

Exam Tip: Entrez, DBGET and LinkDB are generally discussed as text/database retrieval systems, whereas BLAST and FASTA are sequence similarity search tools.

1. What is a Bioinformatics Database Search Engine?

A bioinformatics database search engine is a computational system that allows users to search, retrieve, connect or compare biological information stored in databases.

For example, suppose a researcher has the name of a gene but wants to find its nucleotide sequence. A text-based search system can help locate the corresponding record. If the researcher instead has an unknown DNA sequence and wants to know whether similar sequences are already present in a database, a sequence similarity search tool such as BLAST can be used.

Therefore, database searching is not simply about typing a keyword. The correct search strategy depends on the type of information available to the researcher.

Information Available	Useful Search Approach	Example Tool
Gene name	Text/database search	Entrez
Accession number	Record retrieval	Entrez
Database entry	Database retrieval	DBGET
Related database records	Cross-database linking	LinkDB
Unknown DNA sequence	Sequence similarity search	BLASTN
Unknown protein sequence	Protein similarity search	BLASTP
DNA translated against protein database	Translated similarity search	BLASTX
Protein against translated nucleotide database	Translated similarity search	TBLASTN

2. Major Types of Database Searching

For this syllabus topic, database searching can be understood through two major categories.

A. Text-Based Search Engines

Text-based searching is based mainly on words, names, identifiers, accession numbers, database IDs and other textual information.

Important examples are:

Entrez
DBGET
LinkDB

B. Sequence Similarity-Based Search Engines

Sequence similarity searching starts with a nucleotide or protein sequence and searches a sequence database for similar sequences.

Important examples are:

BLAST
FASTA

Easy way to remember:
Text search asks: "Where is this information?"
Similarity search asks: "Which known sequences look similar to my sequence?"

3. Entrez Search Engine

Entrez is the integrated text search and retrieval system of the National Center for Biotechnology Information (NCBI). It provides a common search interface across many NCBI biological and biomedical databases.

Entrez can be used to search information related to nucleotide sequences, proteins, genes, genomes, structures, genetic variation, gene expression, literature and other biological resources. NCBI describes Entrez as its primary text search and retrieval system integrating literature and molecular databases.

The major advantage of Entrez is that a researcher does not have to remember a completely different search system for every NCBI database. The user can search using terms such as a gene name, protein name, organism, accession number or other identifiers and then move between related records.

Simple Example

Suppose you want information about the human TP53 gene.

You can search NCBI/Entrez using:

TP53
p53
TP53 Homo sapiens
An accession number, if known

The search can lead to relevant records such as Gene, Nucleotide, Protein, Structure, PubMed and other NCBI resources.

4. Important Features of Entrez

Feature	Explanation
Text searching	Search using gene names, protein names, keywords and other terms.
Accession searching	Directly retrieve records using identifiers such as accession numbers.
Cross-database navigation	Move from one related NCBI resource to another.
Boolean searching	Combine search concepts using operators such as AND, OR and NOT.
Filters	Results can often be narrowed by database-specific criteria.
Record retrieval	Detailed biological records can be opened and examined.

Common Entrez Search Concepts

A good database search often depends on using precise search terms. For example:

Basic search: insulin
Organism-specific search: insulin AND Homo sapiens
Gene search: TP53[Gene]
Sequence identifier: accession number

The exact field tags and search syntax depend on the NCBI database being searched, so users should check the help information for the specific database.

5. How to Use Entrez Step by Step

Step 1: Open the NCBI website.

Step 2: Select the appropriate database from the database menu, or use the general search interface.

Step 3: Enter the gene name, protein name, organism, keyword or accession number.

Step 4: Click the search button.

Step 5: Examine the search results and select the relevant record.

Step 6: Open the complete record to examine annotations, sequence information, references and cross-links.

Step 7: Use links to related NCBI databases when additional information is required.

Step 8: Download or export the required biological information when appropriate.

Practical Tip: If you already know an accession number, searching directly with the identifier is usually faster than starting with a broad keyword search.

6. DBGET

DBGET is an integrated database retrieval system developed by the Kanehisa Laboratory and used as a backbone retrieval system for GenomeNet databases, including KEGG.

The basic idea of DBGET is simple: biological databases can be viewed as collections of entries, and individual entries can be retrieved using a database name together with an entry identifier. The official DBGET description represents this concept as:

database:entry

For example, an entry can be specified by combining the relevant database identifier with the entry name. This provides a common retrieval framework for different molecular biology databases.

Why is DBGET Important?

Biological information is distributed across many databases. A researcher may need to move from a gene entry to a protein, from a protein to an enzyme, from an enzyme to a reaction, or from a reaction to a pathway.

DBGET helps provide a systematic way of retrieving database entries across the GenomeNet/KEGG environment.

Basic DBGET Concept

Think of DBGET as a database librarian. You give it the database name and the entry identifier, and the system helps retrieve the corresponding biological record.

Database name + Entry ID → Database Record

7. LinkDB

LinkDB is closely associated with DBGET. While DBGET focuses on retrieving database entries, LinkDB focuses on relationships and links between entries in different databases.

The official DBGET description explains that when two database entries are related, cross-reference information can be represented as a link between the entries. LinkDB is a collection of such links among GenomeNet databases and links to many outside databases.

This is particularly useful because biological information is highly interconnected.

Example Concept

Imagine that you start with a gene. The same biological entity may be connected to:

Gene information
Protein information
Enzyme information
Reaction information
Compound information
Pathway information
Disease information

Instead of treating each record as an isolated piece of information, LinkDB helps users follow relationships among records.

8. DBGET vs LinkDB

Feature	DBGET	LinkDB
Main role	Database entry retrieval	Database entry linking
Main question	Where is this database entry?	What other records are connected to this entry?
Basic concept	Database + Entry	Relationships between entries
Use	Retrieve biological records	Explore cross-references
Association	GenomeNet/KEGG retrieval system	GenomeNet/KEGG cross-database linking

Memory Trick:
DBGET = GET the database entry
LinkDB = LINK related database entries

9. Sequence Similarity-Based Search

Text-based searching works very well when you know the name or identifier of the biological object. But what happens when you have an unknown sequence?

Suppose you sequence a DNA fragment and obtain:

5'-ATGCGTACCGATGCTAGCTGATCG...-3'

You may not know which gene it belongs to. Searching for the exact sequence using a text search engine is not enough. Instead, you need a tool that compares the sequence against many known sequences and identifies regions of similarity.

This is called a sequence similarity search.

Two classical and highly important tools in this area are:

BLAST – Basic Local Alignment Search Tool
FASTA – a family of sequence similarity search and alignment programs

10. BLAST

BLAST stands for Basic Local Alignment Search Tool.

BLAST compares a nucleotide or protein query sequence against sequences in a database and identifies regions of local similarity. NCBI states that BLAST can be used to find sequence similarity and calculate the statistical significance of matches.

BLAST is one of the most widely used tools in molecular biology and bioinformatics.

Why is BLAST called a Local Alignment Tool?

A local alignment looks for the best matching regions between sequences rather than requiring the entire sequences to align from beginning to end.

This is particularly useful when two proteins have a conserved domain but their remaining regions are different.

Example

Suppose you have an unknown protein sequence of 350 amino acids. You run BLASTP and find a strong match with a known enzyme.

You may then investigate:

Percentage identity
Alignment coverage
E-value
Bit score
Conserved residues
Organism information
Functional annotation

Together, these results can help generate a hypothesis about the identity or possible function of the unknown protein.

11. Types of BLAST

BLAST Program	Query	Search Database	Main Use
BLASTN	Nucleotide	Nucleotide	DNA/RNA sequence against nucleotide sequences
BLASTP	Protein	Protein	Protein sequence against protein sequences
BLASTX	Nucleotide translated in six frames	Protein	Find protein-level matches from a nucleotide sequence
TBLASTN	Protein	Translated nucleotide sequences	Search nucleotide databases at the protein level
TBLASTX	Nucleotide translated	Nucleotide translated	Compare translated nucleotide sequences

Easy memory trick:
N = Nucleotide
P = Protein
X = Translation of nucleotide sequence

12. How to Use BLAST Step by Step

The following workflow is useful for students who are using the NCBI BLAST website for the first time.

Step 1: Open the official NCBI BLAST website.

Step 2: Decide whether your query is DNA/nucleotide or protein.

Step 3: Select the appropriate BLAST program.

Step 4: Paste your sequence in the query box or provide an appropriate accession number.

Step 5: Select the database against which you want to search.

Step 6: If required, restrict the search by organism or other database criteria.

Step 7: Choose suitable algorithm settings.

Step 8: Submit the BLAST search.

Step 9: Wait for the search results.

Step 10: Examine the list of matching sequences.

Step 11: Examine identity, query coverage, E-value and alignment score.

Step 12: Open promising hits and inspect their complete database records and annotations.

The NCBI BLAST interface supports sequence input, database selection, organism restrictions and other parameters. For nucleotide BLAST, NCBI also provides choices such as BLASTN and optimized searches for highly similar or more divergent sequences.

13. How to Understand BLAST Results

Getting a BLAST result is only the beginning. A beginner should not simply select the first hit and assume it is the correct answer.

Important BLAST result parameters include:

1. Query Cover

Query coverage tells you how much of your query sequence is represented in the alignment.

For example, if a 500 amino acid protein has an alignment covering 480 residues, the coverage is high. A match covering only 50 residues may represent a small conserved domain rather than the full protein.

2. Percent Identity

Percent identity indicates how many aligned positions contain identical residues.

For protein sequences, a high identity can support a strong evolutionary relationship, but the interpretation depends on sequence length, organism, domain architecture and other evidence.

3. E-value

The E-value, or Expect value, estimates how many matches of a similar score could be expected by chance in a database search under the statistical model used.

In general, a lower E-value indicates stronger statistical significance.

4. Bit Score

The bit score is a normalized alignment score. Higher bit scores generally indicate stronger sequence similarity.

5. Alignment

The actual alignment is extremely important. It allows you to see where residues match, where substitutions occur and where gaps have been introduced.

Parameter	Simple Meaning	General Interpretation
Query Cover	How much of the query aligns	Higher coverage is usually more informative for full-length matches
Identity	Percentage of identical aligned residues	Higher identity generally means greater similarity
E-value	Expected chance matches	Lower is generally more significant
Bit Score	Normalized alignment score	Higher generally indicates a stronger alignment

Important: A high percentage identity alone should not be used to assign function automatically. Sequence coverage, conserved domains, active-site residues, taxonomy and biological context should also be considered.

14. E-value, Score, Identity and Query Coverage

These four terms are frequently asked in bioinformatics examinations and are also essential for interpreting real sequence-analysis results.

E-value

Lower E-value generally indicates that the observed match is less likely to have occurred by chance.

Identity

Identity measures exact residue matches within the alignment.

Similarity

Similarity is broader than identity because chemically or evolutionarily related residues may contribute to a positive substitution score even when the residues are not identical.

Query Coverage

Coverage tells us how much of the query participates in the alignment.

Good BLAST interpretation:
Do not look at only one number. Consider E-value + bit score + identity + query coverage + biological context.

15. FASTA Search Engine

FASTA refers to a family of sequence comparison and alignment programs used for protein and DNA sequence similarity searching.

The FASTA package was developed by William R. Pearson and colleagues and is historically associated with important developments in biological sequence comparison. The current FASTA package provides programs for protein and DNA similarity searching as well as local and global alignment approaches.

FASTA and BLAST both perform sequence similarity searches, but they use different computational strategies.

Why is FASTA Important?

FASTA is important historically and computationally because it provided an efficient heuristic approach for finding sequence similarities. It also includes programs for different types of alignment problems.

The FASTA family includes programs such as:

FASTA – protein/DNA similarity searching
SSEARCH – optimal local alignment based on Smith-Waterman
GGSEARCH – optimal global alignment
GLSEARCH – global query/local library comparison
FASTX/FASTY – translated DNA-to-protein searches
TFASTX/TFASTY – protein query against translated DNA

The official FASTA documentation describes these different programs and their relationships to BLAST-type searches.

16. Principle and Working of FASTA

The basic idea of FASTA is to identify promising regions of similarity efficiently and then perform more detailed comparisons around those regions.

A simplified conceptual workflow is:

Take the query sequence.
Break or examine sequence regions for potential matches.
Identify high-scoring local regions.
Join or refine promising regions.
Calculate alignment scores and statistical significance.
Report the best matching sequences.

The exact algorithmic details depend on the particular FASTA program being used.

FASTA concept:
Fast initial identification of promising sequence regions → detailed comparison → similarity score → statistical assessment → result

17. How to Use FASTA Step by Step

Step 1: Open an appropriate FASTA sequence comparison server or FASTA package.

Step 2: Select whether your query is a protein or DNA sequence.

Step 3: Paste your sequence or select/upload an appropriate input.

Step 4: Select a suitable database.

Step 5: Select the appropriate FASTA program.

Step 6: Set search and scoring parameters if required.

Step 7: Submit the search.

Step 8: Examine the resulting matches.

Step 9: Check alignment quality and statistical significance.

Step 10: Compare the results with BLAST or another appropriate method when confirmation is important.

The official FASTA web interface allows users to select protein or DNA searches, choose databases and adjust several search and scoring options.

19. Complete Bioinformatics Search Workflow

The following SVG summarizes the overall idea of database searching.

18. BLAST vs FASTA

Feature	BLAST	FASTA
Full form	Basic Local Alignment Search Tool	FASTA sequence comparison/search family
Main purpose	Sequence similarity searching	Sequence similarity searching and alignment
Sequences	DNA and protein	DNA and protein
Approach	Heuristic local similarity search	Heuristic similarity search with different algorithms/programs
Common use	Identify similar sequences and infer relationships	Similarity search and specialized alignment analyses
Popular web resource	NCBI BLAST	FASTA server/package
Special feature	Multiple BLAST program types	Includes local/global and translated-search programs

Important: BLAST and FASTA should not be thought of as completely interchangeable tools. They use different computational approaches and provide different program options.

20. Applications of Bioinformatics Database Search Engines

1. Gene Identification

Database searching can help identify genes from known names, accession numbers or sequence similarity.

2. Protein Identification

A protein sequence can be compared against known proteins to identify possible homologs and conserved domains.

3. Functional Annotation

Similarity to experimentally characterized proteins can provide useful evidence for assigning a possible function to an uncharacterized sequence.

4. Homology Searching

Sequence similarity searches can help identify potentially homologous sequences.

5. Evolutionary Studies

Similar sequences from different organisms can be collected for downstream phylogenetic analysis.

6. Genome Annotation

Newly sequenced genomes often contain predicted genes whose functions can be investigated through similarity searches.

7. Conserved Domain Analysis

Sequence-search results can point researchers toward conserved domains and important functional regions.

8. Primer and Experimental Planning

Sequence records retrieved through databases can support primer design and experimental planning.

9. Mutation Analysis

Sequence comparisons can help identify substitutions, insertions and deletions.

10. Research Literature Integration

Entrez allows researchers to connect sequence and molecular records with related scientific literature and other NCBI resources.

21. Advantages

Tool	Major Advantages
Entrez	Integrated NCBI searching, many biological databases, text and identifier-based retrieval.
DBGET	Systematic retrieval of database entries across GenomeNet/KEGG resources.
LinkDB	Helps explore relationships between related database records.
BLAST	Fast and practical sequence similarity searching with multiple programs.
FASTA	Similarity searching plus specialized local/global and translated comparison programs.

22. Limitations

Entrez Limitations

Very broad searches can return a large number of results.
Correct interpretation requires knowledge of the database being searched.
Different NCBI resources have different record structures.

DBGET and LinkDB Limitations

They are most useful when working within interconnected database environments.
The user needs to understand database identifiers and cross-references.
Cross-database links should be interpreted carefully.

BLAST Limitations

A similarity hit does not automatically prove identical biological function.
Short sequences can be difficult to interpret.
Database composition affects the search result.
Highly conserved domains can produce misleading functional assumptions if the complete protein is ignored.

FASTA Limitations

Results depend on the selected program and parameters.
Statistical interpretation requires understanding of sequence similarity methods.
It is not a replacement for biological validation.

Golden Rule: Bioinformatics tools generate evidence and hypotheses. Experimental evidence and biological context are important before making strong functional conclusions.

23. Important Exam Facts

1. Entrez is an integrated text search and retrieval system associated with NCBI.

2. Entrez can search across multiple NCBI biological and biomedical resources.

3. DBGET is an integrated database retrieval system used in the GenomeNet environment and as a backbone retrieval system for KEGG.

4. DBGET uses the concept of database entry retrieval.

5. LinkDB focuses on links and relationships between database entries.

6. BLAST stands for Basic Local Alignment Search Tool.

7. BLAST searches for local regions of sequence similarity.

8. BLASTN compares nucleotide queries with nucleotide databases.

9. BLASTP compares protein queries with protein databases.

10. BLASTX translates a nucleotide query and searches protein sequences.

11. TBLASTN uses a protein query against translated nucleotide sequences.

12. E-value is an important statistical measure in BLAST results.

13. Lower E-values generally indicate more statistically significant matches.

14. Query coverage indicates how much of the query sequence participates in an alignment.

15. FASTA is a family of sequence comparison and alignment programs.

16. SSEARCH is associated with optimal Smith-Waterman local alignment in the FASTA package.

17. GGSEARCH is associated with global alignment.

18. BLAST and FASTA are sequence similarity search approaches but are not identical algorithms.

24. MCQs

MCQ 1

Entrez is primarily associated with:

A. Image processing
B. Text-based database searching
C. Protein crystallization
D. PCR amplification

Answer: B. Text-based database searching

MCQ 2

Which system is associated with NCBI?

A. Entrez
B. DBGET
C. LinkDB
D. FASTA

Answer: A. Entrez

MCQ 3

DBGET is mainly used for:

A. Database entry retrieval
B. PCR primer synthesis
C. Protein crystallization
D. Microscopy

Answer: A. Database entry retrieval

MCQ 4

LinkDB is mainly associated with:

A. Database entry relationships and links
B. DNA sequencing
C. Protein purification
D. Image compression

Answer: A. Database entry relationships and links

MCQ 5

BLAST stands for:

A. Biological Local Analysis Sequence Tool
B. Basic Local Alignment Search Tool
C. Basic Linear Analysis Search Technology
D. Biological Alignment Sequence Technology

Answer: B. Basic Local Alignment Search Tool

MCQ 6

Which BLAST program compares a protein query with protein sequences?

A. BLASTN
B. BLASTP
C. BLASTX
D. TBLASTN

Answer: B. BLASTP

MCQ 7

Which BLAST program searches nucleotide databases using a nucleotide query?

A. BLASTN
B. BLASTP
C. BLASTX
D. TBLASTN

Answer: A. BLASTN

MCQ 8

Which parameter generally becomes more significant as its value decreases?

A. Query length
B. E-value
C. Sequence length
D. Molecular weight

Answer: B. E-value

MCQ 9

FASTA is primarily associated with:

A. Sequence similarity searching and alignment
B. Protein staining
C. DNA extraction
D. Microscopy

Answer: A. Sequence similarity searching and alignment

MCQ 10

Which FASTA program is associated with Smith-Waterman optimal local alignment?

A. GGSEARCH
B. SSEARCH
C. BLASTN
D. FASTX

Answer: B. SSEARCH

25. Frequently Asked Questions

Q1. What is a bioinformatics database search engine?

It is a computational tool used to search, retrieve, connect or compare biological information stored in databases.

Q2. What is Entrez?

Entrez is NCBI's integrated text search and retrieval system for many biomedical and molecular databases.

Q3. What is DBGET?

DBGET is an integrated database retrieval system used in GenomeNet and as a backbone retrieval system for KEGG.

Q4. What is LinkDB?

LinkDB is a cross-database linking system that helps users explore relationships between database entries.

Q5. What is BLAST?

BLAST is a sequence similarity search tool that identifies local similarities between nucleotide or protein sequences and database sequences.

Q6. What is BLASTN?

BLASTN compares a nucleotide query against nucleotide sequence databases.

Q7. What is BLASTP?

BLASTP compares a protein query against protein sequence databases.

Q8. What is FASTA?

FASTA is a family of sequence comparison and alignment programs used for DNA and protein sequence analysis.

Q9. Which is better, BLAST or FASTA?

Neither should be considered universally better. They use different computational approaches and have different program options. BLAST is extremely popular for routine similarity searches, while the FASTA package provides several specialized comparison and alignment methods.

Q10. Does sequence similarity prove gene function?

No. Sequence similarity provides evidence that may support a functional hypothesis, but function should be interpreted using coverage, conserved domains, active sites, taxonomy and other biological evidence.

26. Last-Minute Revision

Text-Based Search Engines
Entrez → NCBI integrated search and retrieval
DBGET → Database entry retrieval
LinkDB → Links between database entries

Sequence Similarity Search Engines
BLAST → Basic Local Alignment Search Tool
FASTA → Sequence similarity and alignment program family

BLAST Programs
BLASTN → Nucleotide vs Nucleotide
BLASTP → Protein vs Protein
BLASTX → Translated Nucleotide vs Protein
TBLASTN → Protein vs Translated Nucleotide
TBLASTX → Translated Nucleotide vs Translated Nucleotide

Important BLAST Parameters
E-value → Statistical significance measure
Bit score → Normalized alignment score
Identity → Exact residue matches
Query coverage → Portion of query aligned

27. Important Official Links

Resource	Official Website	Main Purpose
NCBI	NCBI Official Website	Biomedical and biological information resources
Entrez / NCBI Search	NCBI Search	Search NCBI databases
NCBI BLAST	BLAST	DNA and protein sequence similarity searches
KEGG DBGET	DBGET	Integrated database retrieval
GenomeNet DBGET	GenomeNet DBGET	GenomeNet database retrieval
FASTA	FASTA Sequence Comparison	DNA/protein sequence comparison

28. Conclusion

Bioinformatics database search engines are essential tools for converting large biological datasets into useful scientific information. The most important concept is to understand that different search engines are designed for different kinds of questions.

Entrez is mainly useful when we want to search and retrieve information from the NCBI ecosystem using text, names, identifiers and database records. DBGET provides systematic retrieval of database entries within the GenomeNet/KEGG environment, while LinkDB helps users follow relationships and cross-references between biological database entries.

When the input is a biological sequence rather than a name or identifier, BLAST and FASTA become especially important. BLAST identifies regions of local sequence similarity and provides useful statistical and alignment information. FASTA provides a family of sequence comparison and alignment programs with several specialized approaches.

For students preparing for CSIR-NET, GATE Biotechnology, DBT-BET, ICAR, ICMR-JRF, MSc Biotechnology and Bioinformatics examinations, the most important things to remember are the differences between text-based and sequence-based searching, the functions of Entrez/DBGET/LinkDB, the different BLAST programs, and the interpretation of E-value, identity, bit score and query coverage.

One-Line Revision:
Entrez = Search biological information | DBGET = Retrieve database entries | LinkDB = Connect database entries | BLAST = Search sequence similarity | FASTA = Sequence comparison and alignment

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Tuesday, 8 September 2026