Comprehensive Guide to Bulk Homology Analysis: From Sequence Retrieval to Biological Interpretation
September 27, 2023Performing homology analysis using BLASTP at bulk and then parsing the results into a table requires several steps, including fetching the protein sequences, running BLASTP, and then parsing the output. Here’s a step-by-step guide. This guide assumes you have some knowledge of command-line tools and that you have BLAST and the necessary tools installed on your system.
Table of Contents
Step 1: Downloading Human Proteins in Bulk
- Go to the NCBI Protein Database.
- Enter the query:
"Homo sapiens"[Organism]and apply any other filters you need. - Use the
Send to:link to download the protein sequences in FASTA format.
Alternatively, you can use the NCBI E-utilities on the command line. For example, you can use the esearch and efetch utilities:
esearch -db protein -query "Homo sapiens[Organism]" | efetch -format fasta > human_proteins.fasta
Step 2: Preparing Mouse Protein Database
Before running BLASTP, you need to have a database of mouse proteins that BLAST can search against.
- Download the mouse protein sequences as you did for human proteins, but this time change the organism in your query:sh
esearch -db protein -query "Mus musculus[Organism]" | efetch -format fasta > mouse_proteins.fasta
- Format the mouse protein sequences to create a BLAST database:sh
makeblastdb -in mouse_proteins.fasta -dbtype prot -out mouse_db
Step 3: Running BLASTP in Bulk
Now you can run BLASTP with the human proteins as the query and the mouse proteins as the subject.
blastp -query human_proteins.fasta -db mouse_db -outfmt 6 -out blast_results.txt
Step 4: Parsing BLASTP Results
The BLASTP results are in tabular format, where each row represents a hit and the columns represent different pieces of information about the hit, such as query ID, subject ID, percentage identity, etc.
You can parse this output and create a more readable table using awk, sort, uniq, etc. For example, if you want to get unique hits with their query ID and subject ID:
awk '{print $1, $2}' blast_results.txt | sort | uniq > parsed_blast_results.txt
Step 5: Processing and Analyzing the Results
Depending on your needs, you might want to process the parsed results further, such as filtering the hits based on percentage identity, E-value, or other criteria. You can use command-line tools like awk, sort, grep, etc., or use programming languages like Python or R to process and analyze the results.
Here is a simple Python example to read the BLAST output and filter the results based on percentage identity and E-value.
output_file = open("filtered_blast_results.txt", "w")with open("blast_results.txt", "r") as file:
for line in file:
columns = line.strip().split("\t")
perc_identity = float(columns[2])
e_value = float(columns[10])
if perc_identity >= 30 and e_value <= 1e-5:
output_file.write(line)
output_file.close()
After processing, you may use software like Excel, R, or Python’s pandas library to load your result and perform further analyses or visualize the data.
Troubleshooting:
- Ensure you have sufficient storage and memory, as the protein databases and results can be very large.
- If you run into errors or issues, refer to the BLAST+ User Manual or E-utilities documentation.
Step 6: Analyzing the Results
Once you have filtered the BLAST results, you might want to perform some statistical or comparative analyses on them, depending on your research questions.
Using Python/Pandas for Analysis
Python’s Pandas library can be extremely useful for analyzing tabular data. Below is an example of how you might load your BLAST results into a Pandas DataFrame and perform some basic analyses or transformations:
import pandas as pd# Define column names as per BLAST tabular output format
column_names = [
"qseqid", "sseqid", "pident", "length", "mismatch", "gapopen",
"qstart", "qend", "sstart", "send", "evalue", "bitscore"
]
# Load the BLAST results into a DataFrame
df = pd.read_csv('filtered_blast_results.txt', sep='\t', names=column_names)
# Now you can perform various analyses on this DataFrame
# For example, to get the number of unique hits:
unique_hits = df['sseqid'].nunique()
# Or, to get the average percentage identity of the hits:
average_identity = df['pident'].mean()
print(f"Number of Unique Hits: {unique_hits}")
print(f"Average Percentage Identity: {average_identity}")
Further Statistical Analysis
Depending on your research goal, you may want to perform various types of statistical analyses on the data, such as:
- Comparative Analysis: You might want to compare the distribution of percentage identity, E-values, or other parameters between different protein families or functional groups.
- Correlation Analysis: You might want to study the correlation between different parameters like sequence length, mismatch number, and percentage identity.
- Functional Analysis: Based on the hit proteins, you may want to perform a functional enrichment analysis to see which biological functions or pathways are enriched in your hit list.
Visualization
Using Python’s Matplotlib, Seaborn, or other visualization libraries, you can also visualize your results to better understand the distribution, trends, and patterns in your data.
import matplotlib.pyplot as plt
import seaborn as sns# Set up the aesthetics for the plots
sns.set(style="whitegrid")
# Plotting a histogram of percentage identity
plt.figure(figsize=(10,6))
sns.histplot(df['pident'], bins=30, kde=True)
plt.title('Distribution of Percentage Identity')
plt.xlabel('Percentage Identity')
plt.ylabel('Frequency')
plt.show()
Step 7: Interpretation
After the analysis and visualization, the final step is to interpret the results. Here, your biological knowledge is crucial. Interpretation could involve:
- Identifying Homologous Proteins: Based on high sequence similarity, identify which human proteins have mouse homologs.
- Understanding Evolutionary Relationships: Infer evolutionary relationships between human and mouse proteins.
- Inferring Functional Similarities: Explore if proteins with high sequence similarity also share similar functions or are involved in similar biological processes.
Final Note
This guide provides a basic framework to perform bulk homology analysis using BLASTP and subsequently analyze the results. Depending on your specific research question and dataset, you might need to modify or extend these steps. You can use various bioinformatics tools, programming languages, and statistical methods to gain insights from your BLAST results. And remember, interpreting BLAST results requires a careful consideration of the biological context and the limitations of the method.
Step 8: Advanced Analysis and Biological Interpretation
Domain and Motif Analysis:
Once you have identified homologous proteins, you might want to dig deeper into domain and motif analysis to identify conserved functional or structural elements within the proteins.
- Domain Analysis: Use tools like Pfam or InterProScan to identify conserved domains within your protein sequences.
- Motif Analysis: Employ tools like MEME to discover conserved motifs within the proteins.
Correlation with Other Biological Data:
To gather more insights, you may want to correlate your BLAST results with other types of biological data, such as gene expression, protein-protein interaction networks, or phenotypic data. This can help in building a more comprehensive picture of the biological significance of the observed homologies.
Integrating with Gene Expression Data:
If you have access to gene expression data for the corresponding genes in human and mouse, you could study the expression patterns and see if the homologous proteins show similar expression patterns across different conditions or tissues.
Protein-Protein Interaction Networks:
Studying the interaction networks of the homologous proteins in human and mouse can provide insights into their functional roles and help identify conserved interaction partners or pathways.
Refining Results with Multiple Sequence Alignment:
After initial BLASTP analysis, you can further refine your results using multiple sequence alignment tools like Clustal Omega or MUSCLE to study the detailed sequence conservation and variation among the homologous proteins.
clustalo -i refined_input_sequences.fasta -o output_alignment.aln
Step 9: Reporting and Documentation:
Documenting your analysis steps, results, and interpretations meticulously is critical for transparency and reproducibility.
- Detailed Documentation: Maintain detailed notes of the analysis steps, parameter settings, and any modifications or filtering done to the data.
- Result Compilation: Compile the results in an organized manner, including tables, charts, and any derived data files.
- Clear Interpretation: Clearly state your interpretations, hypotheses, and any conclusions drawn from the analysis. Also, make a note of any limitations or uncertainties in the analysis.
Step 10: Sharing and Collaborating:
Once the analysis is complete and well-documented, it’s important to share the results and insights with collaborators, get feedback, and potentially publish the findings.
- Collaborator Review: Share your findings with your collaborators and discuss the biological implications and relevance of the identified homologies.
- Feedback Integration: Integrate the feedback received from collaborators to refine your analysis and interpretations.
- Publication: Consider publishing your findings in a suitable scientific journal to share your insights with the wider scientific community.
Step 11: Further Exploration:
Post this analysis; you might want to explore more sophisticated methods and tools like phylogenetic analysis, structural analysis, or functional annotation tools to gather deeper insights into the evolutionary, structural, and functional aspects of the identified homologous proteins.
Remember, bioinformatics is an iterative process. New insights often lead to new questions and hypotheses, which can be explored using additional analyses and experiments. Keep refining your analysis approach based on the research question, available data, and emerging insights, and stay updated with the latest tools and methodologies in the field.
