---
title: BLAST quickstart
description: Search a sequence against SwissProt, PDB, or GenBank and read the ranked hits.
---

You want to find sequences similar to your query and know what they are — [BLAST](/docs/blast) does it in a few steps: enter a sequence or accession, pick a program and database (blastp against SwissProt covers most protein work), run the search, and explore the ranked hits with their scores and alignments.

## Webapp input methods

The .bloom* webapp provides a comprehensive interface for BLAST sequence homology searches.

## Input type selection

| Input Type | Description |
|------------|-------------|
| **Enter Sequence or Accession** | Manual FASTA or UniProt accession entry |
| **From Previous Job** | Import protein sequence from ESMFold or other jobs |

### From previous job

- **Job Output Selector**: Browse completed jobs with protein sequence outputs
- **Data type**: Requires `protein_sequence` or `fasta` output
- **Compatible tools**: ESMFold, other protein analysis jobs

## Webapp parameters

### Basic parameters

| Parameter | Options | Default |
|-----------|---------|---------|
| **Program** | blastp, blastn, blastx, tblastn, tblastx | blastp |
| **Database** | swissprot, pdb, nr, nt | swissprot |
| **E-value (EXPECT)** | Numeric | 10 |
| **Max Hits (HITLIST_SIZE)** | Numeric | 100 |

### Program descriptions

| Program | Description |
|---------|-------------|
| **blastp** | Protein vs. protein database |
| **blastn** | Nucleotide vs. nucleotide database |
| **blastx** | Translated nucleotide vs. protein database |
| **tblastn** | Protein vs. translated nucleotide database |
| **tblastx** | Translated query vs. translated database |

### Database descriptions

| Database | Description |
|----------|-------------|
| **swissprot** | UniProtKB/Swiss-Prot (curated) |
| **pdb** | Protein Data Bank |
| **nr** | All non-redundant GenBank |
| **nt** | Nucleotide collection |

### Job options

- **Job Title (optional)**: Custom identifier for tracking
- **Advanced settings…**: Opens settings panel for additional BLAST parameters
- **Use Personalized Settings**: Toggle for project-specific configuration

### Personalized settings

When enabled, loads user-specific BLAST settings from Supabase including:
- Custom scoring matrices
- Composition-based statistics options
- Gap costs and word size preferences

## Submit button

- **Label**: "Run BLAST"
- **Disabled when**: Manual input empty OR no job output selected

## Sequence similarity searching with BLAST in .bloom* platform

## Step-by-step sequence analysis workflow

### 1. Access BLAST search tools

Open **BLAST Sequence Search** in the **Data & Literature** section of .bloom*'s Tools panel, or ask Ivy in chat. You can also submit a BLAST job with `POST /api/v1/jobs` (see the [API reference](/docs/api)) or `ivybloom run`. BLAST provides comprehensive sequence similarity searching with integrated database access and interactive result visualization.

### 2. Prepare your query sequences

**Direct sequence input:**
- **FASTA format**: Paste sequences with headers and descriptions
- **Raw sequences**: Direct amino acid or nucleotide input
- **Previous results**: Use sequences from other .bloom* tools
- **Batch uploads**: Process multiple sequences simultaneously

**Sequence validation:**
- Automatic format detection and validation
- Sequence type recognition (protein vs. nucleotide)
- Length and quality checks
- Preview of parsed sequences

### 3. Choose search parameters

**Database selection:**
- **Supported databases**: swissprot, pdb, nr, nt (see [Basic parameters](#webapp-parameters) above)

**Search program selection:**
- **BLASTP**: Protein vs. protein database
- **BLASTN**: Nucleotide vs. nucleotide database
- **BLASTX**: Translated nucleotide vs. protein database
- **TBLASTN**: Protein vs. translated nucleotide database
- **TBLASTX**: Translated query vs. translated database

### 4. Configure search sensitivity

**Basic parameters:**
- **E-value threshold**: Statistical significance cutoff
- **Max target sequences**: Number of results to return
- **Word size**: Seed length for initial matching
- **Gap penalties**: Alignment gap scoring

**Advanced options:**
- **Scoring matrices**: BLOSUM62, PAM30, etc. for proteins
- **Composition-based statistics**: Low-complexity region handling
- **Filtering options**: Mask low-complexity or repetitive regions
- **Taxonomic restrictions**: Limit searches to specific organisms

### 5. Execute search and explore results

**Real-time search progress:**
- Database scanning progress with time estimates
- Hit accumulation and scoring
- Automatic result ranking and filtering
- Error handling for failed searches

**Interactive results exploration:**
- **Hit table**: Sortable results with scores and statistics
- **Alignment viewer**: Detailed sequence alignments with color coding
- **Taxonomy browser**: Organism distribution analysis
- **Domain visualization**: Conserved domain highlighting

**Result analysis:**
- **Score distributions**: E-value and bit score histograms
- **Identity plots**: Sequence identity vs. position
- **Coverage analysis**: Query coverage statistics
- **Functional annotations**: Gene ontology and pathway links

### 6. Export and downstream analysis

**Export formats:**
- **FASTA files**: Hit sequences for further analysis
- **Alignment files**: Detailed alignment data
- **Tabular results**: CSV/TSV for spreadsheet analysis
- **Graphical reports**: Publication-ready visualizations

**Integration options:**
- **Direct to GraphSol**: Solubility prediction on hits
- **Sequence clustering**: Group similar sequences
- **Multiple alignment**: Align top hits
- **Phylogenetic analysis**: Build evolutionary trees

## Common BLAST search scenarios

### Protein function identification
1. Input novel protein sequence from genomics
2. Search against UniProt/SwissProt database
3. Identify top hits with known functions
4. Analyze conserved domains and motifs
5. Generate functional annotation report

### Homolog discovery
1. Upload protein of interest
2. Search against organism-specific databases
3. Filter by identity and coverage thresholds
4. Identify paralogs and orthologs
5. Analyze evolutionary relationships

### Contamination detection
1. Input sequencing reads or assembled contigs
2. Search against common contaminant databases
3. Identify potential contamination sources
4. Filter and clean sequence datasets
5. Generate contamination reports

### Primer design validation
1. Input primer sequences
2. Search against target genome databases
3. Check specificity and potential off-target binding
4. Optimize primer design based on results
5. Validate primer specificity

## Quality control and validation

### Query preparation checklist

**Sequence quality:**
- [ ] Valid FASTA format with proper headers
- [ ] Correct sequence alphabet (amino acids or nucleotides)
- [ ] Appropriate sequence length for search type
- [ ] No ambiguous characters or gaps

**Search parameters:**
- [ ] Appropriate database selection for query type
- [ ] Reasonable E-value threshold for sensitivity
- [ ] Suitable word size and gap penalties
- [ ] Proper taxonomic or compositional filtering

### Result validation

**Hit quality assessment:**
- [ ] Review top hit E-values and bit scores
- [ ] Check sequence identity and coverage percentages
- [ ] Validate alignment quality and gap patterns
- [ ] Assess biological relevance of top hits

**Statistical validation:**
- [ ] Compare scores across different database searches
- [ ] Check for consistent hit patterns
- [ ] Validate against known positive controls
- [ ] Assess statistical significance of findings

## Advanced search strategies

### Multi-database searching
- Search across multiple databases simultaneously
- Combine results from different sequence collections
- Cross-validate hits across database types
- Generate comprehensive similarity profiles

### Iterative refinement
- Use top hits as queries for follow-up searches
- Refine search parameters based on initial results
- Expand or restrict taxonomic scope
- Adjust sensitivity for specific discovery goals

### Comparative analysis
- Search same query against different databases
- Compare hit distributions across organisms
- Identify database-specific vs. universal hits
- Generate comparative similarity reports

## Integration with .bloom* workflows

### Upstream sequence sources

**Genomics data:**
- **Assembled contigs**: Genome assembly outputs
- **Transcript sequences**: RNA-seq derived sequences
- **Protein predictions**: Gene finding and translation results
- **Metagenomic data**: Environmental sequencing samples

**Protein data:**
- **Novel proteins**: Discovery proteomics results
- **Predicted structures**: ESMFold sequence inputs
- **Mutant libraries**: Engineered sequence variants
- **Synthetic designs**: De novo protein sequences

### Downstream applications

**Functional annotation:**
- **Gene ontology mapping**: Link sequences to biological functions
- **Pathway analysis**: Identify metabolic and signaling pathways
- **Domain identification**: Find conserved structural elements
- **Motif discovery**: Identify regulatory sequence patterns

**Comparative analysis:**
- **Phylogenetic studies**: Build evolutionary relationships
- **Sequence clustering**: Group similar sequences
- **Conservation analysis**: Identify functionally important regions
- **Species distribution**: Analyze taxonomic distribution patterns

**Structure prediction:**
- **Homology modeling**: Use templates from BLAST hits
- **Domain prediction**: Identify structural domain boundaries
- **Secondary structure**: Predict folding patterns
- **Functional site identification**: Locate active sites and interfaces

## Performance optimization

### Search efficiency

**Database selection:**
- Choose appropriate database size for query scope
- Use taxonomic restrictions to limit search space
- Select program type matching query-database types
- Consider pre-filtered databases for specific applications

**Parameter optimization:**
- Adjust E-value thresholds for desired sensitivity
- Set appropriate max target sequences
- Use composition-based statistics for biased sequences
- Enable filtering for repetitive or low-complexity regions

### Result processing

**Batch processing:**
- Process multiple queries simultaneously
- Use result filtering to manage output volume
- Implement result caching for repeated searches
- Automate downstream analysis pipelines

**Output management:**
- Configure appropriate output formats
- Use tabular formats for programmatic analysis
- Generate summary statistics automatically
- Implement result filtering and prioritization

## Best practices for sequence similarity searching

### Query preparation

**Sequence optimization:**
- Use high-quality, full-length sequences when possible
- Remove vector sequences and adapters
- Mask repetitive or low-complexity regions
- Consider domain-specific searches for multi-domain proteins

**Parameter selection:**
- Start with default parameters for initial searches
- Adjust sensitivity based on sequence conservation
- Use appropriate substitution matrices
- Consider query length and composition

### Result interpretation

**Biological context:**
- Interpret results in context of known biology
- Cross-reference with literature and databases
- Consider experimental validation approaches
- Assess functional relevance of hits

**Statistical considerations:**
- Understand E-value and bit score meanings
- Consider multiple testing corrections
- Account for database composition effects
- Validate against known reference sequences

### Research reproducibility

**Documentation:**
- Record all search parameters and settings
- Preserve input sequences and database versions
- Document result interpretation criteria
- Create analysis notebooks for methods

**Collaboration:**
- Share search strategies and parameters
- Generate standardized result reports
- Document hit validation approaches
- Maintain searchable result archives

## Troubleshooting common issues

### No significant hits
- Lower E-value threshold for more sensitivity
- Check sequence quality and remove contaminants
- Try different substitution matrices
- Search against more comprehensive databases

### Too many hits
- Raise E-value threshold for specificity
- Use taxonomic restrictions
- Apply compositional filtering
- Consider domain-specific searches

### Poor alignment quality
- Check sequence quality and correct errors
- Use appropriate gap penalties
- Try different alignment parameters
- Consider if sequences are too divergent

### Performance issues
- Use smaller databases for initial searches
- Implement result limits
- Consider iterative search strategies
- Use pre-filtered databases when appropriate

## Read next