# How to Find and Analyze a Gene of Interest This guide walks you through the complete workflow of finding a gene of interest and characterizing it using BLAST for sequence similarity searching and UniProt for functional annotation. By the end, you will know how to take a raw sequence, identify its closest matches, extract curated functional information, map protein domains, place your gene in an evolutionary context, and connect it to broader biological datasets. If you are starting to learn bioinformatics, this is one of the most foundational workflows you will use repeatedly throughout a [bioinformatics course](/blog/careers/bioinformatics-courses-and-certifications-a-practical-guide) or independent research project. You will need a computer with internet access, a text editor for handling FASTA files, and optionally the BLAST+ command-line suite installed locally. Everything described here uses free, publicly available tools maintained by NCBI and EMBL-EBI. ## Understanding BLAST and UniProt ### BLAST: Sequence Similarity Search BLAST (Basic Local Alignment Search Tool) is an algorithm that finds regions of similarity between biological sequences. It compares a query sequence against a database of known sequences and reports statistically significant matches. BLAST is fast because it breaks sequences into short words, identifies exact or near-exact word matches in the database, and then extends those matches into longer alignments scored for [statistical significance](/blog/guides/statistical-significance). The algorithm produces three key metrics for each hit: - **Bit score** reflects the quality of the alignment. Higher scores indicate better matches. A bit score above 50 is generally meaningful, and scores above 100 usually indicate close homology. - **E-value** estimates the number of matches you would expect to find by chance in a database of that size. An E-value of 0.001 means you would expect one match this good by random chance in a database of 1,000 database-sized searches. Lower E-values indicate more significant matches. - **Percent identity** is the fraction of aligned positions with identical residues. High identity (above 70%) over a long alignment strongly suggests homology. ### UniProt: Functional Annotation UniProt (Universal Protein Resource) is a comprehensive database of [protein sequence](/blog/guides/protein-sequence) and functional information maintained by a consortium including EMBL-EBI. It combines expertly reviewed entries (Swiss-Prot) with automatically annotated entries (TrEMBL). Reviewed entries carry the designation "reviewed" and include functional descriptions, domain architecture, subcellular location, post-translational modifications, disease associations, variants, and cross-links to dozens of other databases including PDB for structural data, Pfam for protein domains, and Gene Ontology for functional classification. Researchers use UniProt as a starting point for understanding what a protein does. In one study on type 2 diabetes mellitus, membrane protein-related genes were sourced from the UniProt website alongside GEO expression data, then analyzed with GO enrichment, KEGG pathway analysis, and protein-protein interaction networks to identify diagnostic biomarkers [1]. In another application, a large language model platform called GeneReL used UniProt annotations as part of a five-step gene normalization pipeline for disambiguating gene names extracted from the literature [2]. ## Prerequisites and Setup For web-based work, you need only a browser. Bookmark these two URLs: - NCBI BLAST: https://blast.ncbi.nlm.nih.gov/ - UniProt: https://www.uniprot.org/ For command-line work on Linux or macOS, install the BLAST+ suite: ```bash # On Ubuntu/Debian sudo apt-get install ncbi-blast+ # On macOS with Homebrew brew install blast ``` Verify the installation: ```bash blastp -version ``` Expected output: ``` blastp: 2.15.0+ Package: blast 2.15.0, build Jul 18 2023 16:36:18 ``` You should also install `curl` for making API requests and `jq` for parsing JSON output: ```bash # On Ubuntu/Debian sudo apt-get install curl jq # On macOS with Homebrew brew install curl jq ``` ## How to Analyze a Gene Using BLAST and UniProt Step by Step The workflow below assumes you have a protein sequence for your gene of interest. If you start with a [nucleotide sequence](/knowledge/molecular-biology/nucleotide-sequence), see the note on BLAST program selection in Step 2. ### Step 1: Obtain Your Starting Sequence Your starting point may be a gene name (like TP53), an accession number (like P04637), a FASTA-formatted sequence from a sequencing run, or a short read from a transcriptome assembly. Each starting point requires a slightly different entry into the workflow. If you already have a protein sequence in FASTA format, verify that it looks correct: ```bash head my_gene.fasta ``` Expected output: ``` >my_gene_of_interest MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGP DEAPRMPEAAPPVAPAAPAAPAPTADAPAPADSWELSSPPNEPLMPSEDAPPLWILDNLF ... ``` A valid FASTA record has a header line starting with `>` followed by the sequence on subsequent lines. Protein sequences use the single-letter amino acid code. If your sequence contains characters other than the 20 standard amino acid codes plus X (for unknown), check the source of your data for formatting errors. If you have a gene name or accession but need the sequence, download it from UniProt: ```bash # Download a protein sequence by UniProt accession curl "https://rest.uniprot.org/uniprotkb/P04637.fasta" -o p53.fasta # Verify the download head -5 p53.fasta ``` Before diving into similarity searches, it helps to understand your gene's basic structure. For any gene you plan to work with experimentally, determining the exact start and end of the coding region and its exon/intron boundaries is a prerequisite for downstream applications like [expression vector](/knowledge/molecular-biology/expression-vector) construction [3]. ### Step 2: Choose the Right BLAST Program BLAST comes in five flavors depending on whether your query and database are nucleotide or protein sequences: | Program | Query Type | Database Type | When to Use | |-----|------|--------|-------| | blastn | Nucleotide | Nucleotide | Comparing DNA sequences directly | | blastp | Protein | Protein | Finding similar proteins (most common) | | blastx | Nucleotide | Protein | Translating a DNA query to find proteins | | tblastn | Protein | Nucleotide | Finding genes in unannotated genomes | | tblastx | Nucleotide | Nucleotide | Both translated, for finding distant gene relationships | Choose blastp when you have a protein sequence and want to identify it or find homologs. Protein-level comparisons are more sensitive than nucleotide-level comparisons because the genetic code is degenerate (multiple codons encode the same amino acid), so protein sequences reflect evolutionary conservation more directly. The choice between blastx and blastp matters more than many beginners realize. In one study comparing downstream analyses of transcriptome data, protein sequences validated by mass spectrometry and analyzed with BLASTp produced more identified BLAST hits, more Gene Ontology terms, and more KEGG pathway enzyme annotations than the equivalent nucleotide sequences analyzed with BLASTx [4]. When you have the option, work at the protein level. ### Step 3: Run the BLAST Search **Option A: NCBI Web BLAST** Go to https://blast.ncbi.nlm.nih.gov/. Choose "Protein BLAST" (blastp). Paste your FASTA-formatted protein sequence into the query box. Select the database: - **Swiss-Prot (swissprot)** for curated, non-redundant protein sequences from reviewed UniProt entries. This is the best choice for a first search because results are high quality and manageable in number. - **Non-redundant protein sequences (nr)** for comprehensive coverage including unreviewed predictions. This database is enormous and will return many more hits. - **Reference proteins (refseq_protein)** for curated sequences from specific organisms. Click "BLAST." Results typically appear within seconds to a few minutes depending on database size and server load. **Option B: Command-Line BLAST Against Local Databases** For reproducible pipelines, run BLAST locally. First, create a database from a FASTA file of reference sequences: ```bash # Build a protein database from your reference sequences makeblastdb -in reference_proteins.fasta -dbtype prot -out my_reference_db -title "Reference Proteins" ``` Expected output: ``` Building a new DB, current sequence: 1 Type of file: protein Successfully added 1 sequences in 0.002 seconds. ``` Then run the search: ```bash blastp -query my_gene.fasta -db my_reference_db -evalue 1e-5 -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore" -out blast_results.tsv -num_threads 4 ``` **Option C: Remote BLAST Against NCBI Databases** You can run BLAST against NCBI-hosted databases directly from the command line using the `-remote` flag: ```bash blastp -query my_gene.fasta -db swissprot -remote -evalue 1e-5 -outfmt "6 qseqid sseqid pident length evalue bitscore" -out remote_results.tsv ``` The `-remote` flag sends your query to NCBI servers. This is convenient but slower than local searches and subject to NCBI usage policies (avoid submitting large batches via `-remote`). ### Step 4: Interpret BLAST Results ![Worked example of an E-value of 0.001, one expected chance match per 1,000 database-sized searches, and the 1e-5 cutoff for protein searches.](/article-images/illustrations/how-to-analyze-a-gene-using-blast-and-uniprot-evalue-threshold.webp) Examine the output file: ```bash column -t -s $'\t' blast_results.tsv | head -10 ``` A representative tabular output (format 6) looks like this: ``` my_gene sp|P04637|P53_HUMAN 100.000 393 0 0 0.0 807 my_gene sp|P02340|P53_MOUSE 87.287 385 44 4 1e-178 628 my_gene sp|P02341|P53_RAT 85.487 379 50 3 2e-165 583 my_gene sp|P79734|P53_PIG 82.189 382 63 4 3e-140 504 ``` Read each column as follows: 1. **qseqid** is your query sequence identifier. 2. **sseqid** is the subject (database hit) identifier. The UniProt-style ID includes the accession and entry name. 3. **pident** is percent identity across the aligned region. 4. **length** is the alignment length in amino acids. 5. **mismatch** is the number of mismatched positions. 6. **gapopen** is the number of gap-opening events. 7. **qstart, qend, sstart, send** define the coordinates of the alignment in query and subject. 8. **evalue** is the expect value. Lower means more significant. 9. **bitscore** is the normalized alignment score. Higher means better. The top hit at 100% identity over the full query length is an exact match to the database sequence. Subsequent hits at progressively lower identity represent homologs from other species. Pay attention to alignment coverage. A hit with 95% identity over only 50 amino acids is far less informative than one with 80% identity over 380 amino acids. The former might represent a shared domain, not a true ortholog. BLAST has been applied to detect specific mutations in clinical contexts. In a study of IL-17A in Iraqi breast cancer patients, researchers used BLAST to identify deletion and missense mutations in blood samples from 60 patients compared to 20 healthy controls, then correlated those mutations with altered protein physicochemical properties and structure [5]. ### Step 5: Retrieve Functional Annotation from UniProt Now that BLAST has identified your gene, query UniProt for its annotated function. **Web interface:** Go to https://www.uniprot.org/ and search for the accession number or gene name. Filter results by organism and by "Reviewed" status to get Swiss-Prot quality annotations. The entry page shows function, subcellular location, domain architecture, sequence features, PTMs, variants, and cross-references. **REST API for programmatic access:** ```bash # Retrieve the full entry in JSON format curl -s "https://rest.uniprot.org/uniprotkb/P04637.json" -o p53_uniprot.json # Extract the recommended protein name curl -s "https://rest.uniprot.org/uniprotkb/P04637.json" | jq -r '.proteinDescription.recommendedName.fullName.value' ``` Expected output: ``` Cellular tumor antigen p53 ``` ```bash # Get the sequence length and molecular mass curl -s "https://rest.uniprot.org/uniprotkb/P04637.json" | jq '{length: .sequence.length, mass: .sequence.mass}' ``` Expected output: ```json { "length": 393, "mass": 43653 } ``` ```bash # List domain annotations curl -s "https://rest.uniprot.org/uniprotkb/P04637.json" | \ jq '[.features[] | select(.type == "Domain") | {name: .description, start: .location.start.value, end: .location.end.value}]' ``` Expected output: ```json [ { "name": "P53", "start": 94, "end": 292 } ] ``` ```bash # Search UniProt for all reviewed human proteins matching a keyword curl -s "https://rest.uniprot.org/uniprotkb/search?query=organism_id:9606+AND+reviewed:true+AND+gene:TP53&format=tsv&fields=accession,id,protein_name,gene_names" | column -t -s $'\t' ``` The TSV output includes columns for accession, entry ID, protein name, and gene names, giving you a quick overview of all matching entries. UniProt is also the reference point for selecting experimental reagents. For the SMPD1 gene encoding acid sphingomyelinase (UniProt ID P17405), a characterization study used the UniProt accession as the stable identifier around which to organize antibody validation for western blot and immunoprecipitation experiments [6]. ### Step 6: Analyze Protein Domains and Properties Protein domains are conserved structural and functional units. Identifying them tells you what molecular activities your protein likely performs. **Domain identification with InterPro/Pfam** The InterPro database (https://www.ebi.ac.uk/interpro/) integrates Pfam, SMART, PROSITE, and other domain databases. UniProt entries already include InterPro domain annotations in their cross-references. For large-scale or custom analysis, use HMMER to search Pfam hidden Markov models: ```bash # Download the Pfam-A HMM database (large file, ~1.5 GB compressed) wget "https://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz" gunzip Pfam-A.hmm.gz # Search your protein against Pfam hmmscan --domtblout pfam_results.txt Pfam-A.hmm my_protein.fasta ``` This approach was used in a study of peptidase gene families in Rhodnius prolixus, where researchers screened 433 protease-coding genes across 71 families using NCBI BLAST alongside HMMER and PFAM domain analysis to identify expansions associated with blood-feeding adaptation [7]. **Physicochemical property analysis** Compute basic physicochemical properties using ProtParam on the ExPASy server (https://web.expertise.expassy.org/protparam/). Input your protein sequence and the tool returns: - Molecular weight - Theoretical isoelectric point (pI) - Amino acid composition - Grand average of hydropathicity (GRAVY) - Instability index - Aliphatic index These properties inform predictions about solubility, stability, and localization. In the IL-17A breast cancer study, researchers determined that IL-17A is hydrophilic in nature, has alpha-helical secondary structure, and that mutations in the gene affect these physicochemical properties of the protein [5]. For more advanced protein feature analysis, newer computational frameworks are moving beyond simple amino acid identity. A PhysioChem-K-mer framework that encodes physicochemical properties directly into feature spaces achieved 81.33% classification accuracy on synthetic benchmarks and 64.63% on real UniProt/Swiss-Prot data using a hydropathy-based representation, outperforming standard 3-mer methods [8]. ### Step 7: Explore Homologs and Evolutionary Context Understanding where your gene sits in an evolutionary framework clarifies its function. Homologs in other species often have experimentally characterized functions that can be transferred to your gene of interest. **Collect homologs from BLAST results** Take the top 20 to 50 non-redundant hits from your BLAST search. Filter out: - Hits below 30% identity (too divergent to be informative) - Hits covering less than 50% of the query length - Duplicate sequences from the same species - Predicted or hypothetical proteins (unless the hit is highly significant) **Build a [multiple sequence alignment](/blog/guides/multiple-sequence-alignment)** ```bash # Align homologous sequences with MAFFT mafft --auto homologs.fasta > homologs_aligned.fasta ``` MAFFT automatically selects the best alignment strategy based on sequence number and length. For most protein families with fewer than 200 sequences, it completes in minutes. **Build a phylogenetic tree** ```bash # Build a maximum-likelihood tree with IQ-TREE iqtree -s homologs_aligned.fasta -m MFP -bb 1000 -nt 4 ``` The `-m MFP` flag runs ModelFinder to select the best substitution model. The `-bb 1000` flag runs 1,000 ultrafast bootstrap replicates to assess branch support. The resulting tree file (`.treefile` in Newick format) can be visualized in iTOL (https://itol.embl.de/), FigTree, or Dendroscope. **Why phylogenetic context matters** In a study of the catenin gene family, researchers used BLAST searches against public databases combined with Bayesian phylogenetic inference to demonstrate that beta and p120 catenins form a monophyletic clade based on sequence and structural similarity, while alpha catenin belongs to a different superfamily entirely. Their phylogenetic analyses also revealed multiple [gene duplication](/knowledge/molecular-biology/gene-duplication) events throughout metazoan evolution and uncovered annotation issues in existing databases [9]. A recent tool called BAT (BLAST-Align-Tree) automates this entire pipeline by combining a BLAST search of local genome databases with a gene tree construction pipeline that enables multiple annotation modes. BAT displays experimental datasets, custom amino acid motifs, and protein HMM domain annotations on gene trees, and runs locally with support for custom genomes [10]. In plant research, phylogenetic analysis combined with molecular cloning revealed that the wheat blast resistance gene Rmg8 is a homoeologue of the powdery mildew resistance gene Pm4, with variants carrying the ability to recognize the AVR-Rmg8 avirulence gene distributed across Triticum and Aegilops species. The evolutionary analysis traced the origin of this resistance specificity to a time approximately 5 million years before the emergence of its current fungal target [11]. ### Step 8: Connect to Expression Data and Pathways Sequence and domain information tell you what your protein might do. Expression data tells you when and where it does it. **[Gene Expression Omnibus](/blog/guides/gene-expression-omnibus) (GEO)** The NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/) hosts thousands of transcriptomic datasets. Search for studies involving your gene or disease context. Download raw count matrices and analyze differential expression with tools like DESeq2 or edgeR. **[Pathway enrichment analysis](/blog/guides/pathway-enrichment-analysis-turning-a-gene-list-into-testable-questions)** Once you have a set of genes (for example, co-expressed genes or genes in a cluster), run enrichment analysis to identify overrepresented biological themes: ```r # R example using clusterProfiler library(clusterProfiler) library(org.Hs.eg.db) gene_list <- c("TP53", "CDKN1A", "BAX", "MDM2") ego_result <- enrichGO(gene = gene_list, OrgDb = org.Hs.eg.db, keyType = "ENTREZID", ont = "BP", pAdjustMethod = "BH", pvalueCutoff = 0.05) head(as.data.frame(ego_result)) ``` This produces a table of enriched Gene Ontology biological process terms with adjusted p-values, gene counts, and fold enrichment. In the type 2 diabetes study, researchers sourced membrane protein-related genes from UniProt and expression data from GEO, then applied GO analysis, KEGG pathway enrichment, and protein-protein interaction network analysis to identify differentially expressed membrane protein-related genes between normal and T2DM groups [1]. They subsequently used machine learning algorithms (Gaussian Mixture Model, Random Forest, SVM) to narrow down hub genes and constructed a diagnostic model validated by ROC curves and experimental assays. In COPD research, investigators applied six feature selection algorithms (LASSO, LightGBM, MCFS, RF, CATboost, and XGBoost) with three classification methods to single-cell data and identified FKBP5 and SCGB3A1 as promising marker genes with strong correlations to disease status across immune and epithelial-stromal cell populations [12]. A similar integrative approach in Huntington's disease research combined GEO datasets with LASSO, Random Forest, SVM-RFE, Mendelian randomization, and single-cell RNA-seq analysis to identify ADRA2A as a decreased gene in HD, along with transcription factors SOX4 and ETV1 and multiple heparan sulfate sulfotransferase genes driving cell differentiation [13]. ## Key BLAST Parameters Reference | Parameter | Command-Line Flag | What It Controls | Recommended Setting | |------|----------|---------|-----------| | Expect value | `-evalue` | Significance threshold for reporting hits | 1e-5 for initial searches | | Word size | `-word_size` | Seed match length for initial hits | 6 (protein), 11 (nucleotide) | | Scoring matrix | `-matrix` | Substitution scoring scheme | BLOSUM62 (default, works for most cases) | | Max target sequences | `-max_target_seqs` | Number of database hits to report | 100-500 | | Output format | `-outfmt` | Structure of results file | 6 for tabular, 0 for pairwise | | Number of threads | `-num_threads` | CPU cores used | Match available cores | | Low-complexity filter | `-seg` / `-dust` | Mask low-complexity regions | Yes (default) | | Database type | `-dbtype` | Protein or nucleotide | Set during makeblastdb | ## Common Mistakes and Limitations **Mistake 1: Trusting percent identity alone.** Two sequences can share 90% identity over 30 amino acids and have no meaningful relationship, while two sequences sharing 35% identity over 400 amino acids are likely homologs. Always check alignment length and E-value alongside identity. A short, high-identity hit to a conserved domain is not evidence of orthology. **Mistake 2: Using the wrong BLAST program.** Running blastn with a protein sequence or blastp with a nucleotide sequence produces errors or meaningless results. If your query is a nucleotide sequence from a transcript or genomic region, use blastx to translate it in all six reading frames and compare against a protein database [4]. **Mistake 3: Ignoring low-complexity regions.** Regions with biased amino acid composition (poly-alanine tracts, serine-rich stretches) produce spurious BLAST hits. Keep the SEG filter (for protein) or DUST filter (for nucleotide) enabled at their default settings. If you get hits to many unrelated proteins all aligned to the same region of your query, that region is likely low-complexity. **Mistake 4: Using nr when swissprot would serve better.** The nr database includes computationally predicted proteins that may lack experimental validation. A search against swissprot returns fewer hits but each one is expertly reviewed. Start with swissprot, then broaden to nr if you get no significant hits. **Mistake 5: Not verifying gene structure before primer design.** If your downstream goal involves cloning or expressing your gene, you must determine the exact start and end of the coding region and exon/intron boundaries before designing oligonucleotides. Working from a predicted annotation without checking against evidence (RNA-seq data, homologous gene structures) can lead to amplifying the wrong transcript variant [3]. **Mistake 6: Assuming top BLAST hit equals function.** BLAST finds similar sequences. It does not prove functional equivalence. Paralogs (genes related by duplication within a genome) may have diverged in function despite sequence similarity. Phylogenetic analysis helps distinguish orthologs (same function, different species) from paralogs (potentially different function, same or different species). **Mistake 7: Skipping the reviewed/unreviewed distinction in UniProt.** UniProt contains both expertly reviewed Swiss-Prot entries and automatically generated TrEMBL entries. TrEMBL annotations are predictions based on similarity rules and can be wrong. Always check whether your entry is marked "Reviewed" before trusting its functional annotations. **Mistake 8: Not considering isoforms.** Many genes produce multiple protein isoforms through alternative splicing. UniProt lists all known isoforms for a given entry. Make sure the isoform you are analyzing matches the one relevant to your biological question. ## Practical Tips for Better Results **Tip 1: Search swissprot first, then nr.** A curated database gives you cleaner, more interpretable results. If your sequence is novel or from a poorly characterized organism, you may need to broaden to nr, but starting with swissprot avoids drowning in predicted protein entries. **Tip 2: Use outfmt 6 for downstream processing.** Tabular output (format 6) is easy to parse with awk, sort, or Python. Define only the columns you need to keep file sizes small. **Tip 3: Verify hits by reciprocal BLAST.** Take your top hit, use it as a query, and BLAST it back against a database containing your original organism. If the best match is your original gene, you have strong evidence of orthology. This reciprocal best hit approach is a standard method for ortholog identification. **Tip 4: Batch queries when working with multiple genes.** Instead of running BLAST one gene at a time, concatenate multiple query sequences into a single FASTA file. BLAST processes each independently and labels results by query ID, saving significant time. ```bash # Combine multiple query sequences cat gene1.fasta gene2.fasta gene3.fasta > all_queries.fasta # Run a single BLAST search for all queries blastp -query all_queries.fasta -db swissprot -remote -outfmt 6 -evalue 1e-5 -out all_results.tsv ``` **Tip 5: Cross-reference multiple databases.** A single UniProt entry links to dozens of other resources. Follow the cross-references to Pfam for domains, PDB for structures, STRING for protein interactions, and KEGG for metabolic pathways. Building a complete picture requires triangulating across these resources. **Tip 6: Document your database versions.** When you publish results, record the BLAST+ version number, database version or download date, and all parameter settings. This makes your analysis reproducible. Databases change constantly, and a search against nr from January will yield different results than one from August. **Tip 7: For gene families, automate.** If you are analyzing a gene family rather than a single gene, consider a pipeline approach. The BAT pipeline (BLAST-Align-Tree) automates the steps of BLAST searching, sequence alignment, tree construction, and annotation overlay, supporting project-specific resources and custom genomes [10]. For ad hoc analysis, a shell script combining the commands shown in this article achieves the same result with more manual control. ## Frequently Asked Questions ### What is the difference between BLAST and UniProt? BLAST is a search algorithm that finds sequences similar to your query. UniProt is a curated database that stores protein sequences along with experimentally validated functional annotations. You use BLAST to find what your sequence resembles and UniProt to learn what that protein does. ### Which BLAST program should I use for a nucleotide sequence? Use blastx if you want to find proteins encoded by your DNA sequence. It translates your nucleotide query in all six reading frames and compares the results against a [protein database](/blog/guides/protein-database). Use blastn if you want to find similar DNA sequences directly, such as identifying a species from a barcode sequence. ### What does an E-value mean in BLAST results? The E-value estimates how many matches with a score at least this good you would expect to find by chance in a database of the size searched. An E-value of 0.001 means approximately one false positive per 1,000 searches. Values below 1e-5 are generally considered significant for protein searches. ### How do I download a protein sequence from UniProt? Go to the UniProt entry page and click the "Download" button, or use the REST API with a command like `curl "https://rest.uniprot.org/uniprotkb/P04637.fasta" -o output.fasta`, replacing P04637 with your accession of interest. ### Can I use BLAST against a specific organism only? Yes. On the web BLAST interface, use the "Organism" field to restrict results. On the command line, use the `-entrez_query` flag with `-remote` (for example, `-entrez_query "Mus musculus[ORGN]"`) or build a custom local database containing only your organism's sequences. ### What does "hypothetical protein" mean in my BLAST results? It means the sequence was predicted computationally from genome annotation but has no experimental evidence for expression or function. These entries lack curated functional information. Treat them cautiously and rely on your own domain and phylogenetic analysis for inference. ### How many BLAST hits should I trust? There is no fixed number. Focus on hits with E-values below your threshold, alignment coverage above 50% of your query, and percent identity appropriate for your taxonomic distance. The top 10 to 50 non-redundant hits usually suffice for characterizing a gene. ### Do I need programming skills to use BLAST and UniProt? No. Both offer web interfaces that require no programming. Command-line tools become useful for reproducible pipelines, batch processing, and integration with other analysis steps. Start with the web interface and add command-line skills as your projects grow in complexity. ## Sources 1. [Machine learning and bioinformatics identify and validate membrane protein biomarkers for type 2 diabetes mellitus.](https://pubmed.ncbi.nlm.nih.gov/42568355/) 2. [GeneReL: a large language model-powered platform for gene regulatory relationship extraction with community curation.](https://pubmed.ncbi.nlm.nih.gov/42841320/) 3. [In Silico Gene Analysis and Oligonucleotide Design for the Construction of Expression Vectors.](https://pubmed.ncbi.nlm.nih.gov/33165794/) 4. [Refining Transcriptome Gene Catalogs by MS-Validation of Expressed Proteins.](https://pubmed.ncbi.nlm.nih.gov/29152876/) 5. [Analytical Computation of Interleukin17A Activity in Breast Cancer Patients Using Bioinformatics Methods.](https://pubmed.ncbi.nlm.nih.gov/35096325/) 6. [A guide to selecting high-performing antibodies for ASM (UniProt ID: P17405) for use in western blot and immunoprecipitation.](https://pubmed.ncbi.nlm.nih.gov/42964033/) 7. [Genome Wide Mapping of Peptidases in Rhodnius prolixus: Identification of Protease Gene Duplications, Horizontally Transferred Proteases and Analysis of Peptidase A1 Structures, with Considerations on Their Role in the Evolution of Hematophagy in Triatominae.](https://pubmed.ncbi.nlm.nih.gov/29326597/) 8. [A theoretical analysis of PhysioChem-K-mer features for protein classification using controlled synthetic benchmarks.](https://pubmed.ncbi.nlm.nih.gov/42426114/) 9. [The evolutionary history of the catenin gene family during metazoan evolution.](https://pubmed.ncbi.nlm.nih.gov/21740572/) 10. [BAT: an integrated pipeline for gene tree construction, annotation, and functional inference.](https://pubmed.ncbi.nlm.nih.gov/42182278/) 11. [Evolution of wheat blast resistance gene Rmg8 accompanied by differentiation of variants recognizing the powdery mildew fungus.](https://pubmed.ncbi.nlm.nih.gov/38898164/) 12. [Identification of Transcriptional Signatures Associated with COPD in Immune and Epithelial-Stromal Cells Using Machine Learning Methods.](https://pubmed.ncbi.nlm.nih.gov/42942315/) 13. [Bioinformatics Analysis to Reveal Potential Targets of Huntington's Disease.](https://pubmed.ncbi.nlm.nih.gov/42967576/)