Pan-CRISPR Genome Scanning for New Cas Effectors

ISEF Category: Microbiology

Subcategory: Microbial Genetics  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Bacteria and archaea carry natural defense systems that can turn into gene-editing tools. Some of those tools are tiny, which makes them easier to package and use later. You can search for them across thousands of genomes, then rank the best-looking candidates with structure prediction. That turns public data into a real discovery project.

What Is It?

CRISPR systems are the immune systems of many microbes. They store bits of viral DNA, then use Cas proteins, which are CRISPR-associated proteins, to find and cut matching genetic material. Your project asks a simple question with a big data layer, where do new-looking Cas proteins hide across many genomes?

Think of it like scanning a giant library for unusual lock-and-key tools. CRISPRCasFinder helps you spot CRISPR regions and nearby Cas genes. GTDB, the Genome Taxonomy Database, gives you a large set of representative microbial genomes to search. ESMFold then predicts protein shape from sequence, so you can see whether a candidate effector looks compact and plausible for future editing work.

Why This Is a Good Topic

This makes a strong science fair topic because it starts with public data, but still leaves room for original analysis. You can define your own search rules, compare architectures across groups, and build a ranking system for compact candidate effectors. The work connects to genome editing, protein structure, and microbial evolution, so it has a real-world payoff. You can also finish with clear figures, tables, and a reproducible pipeline, which judges like.

Research Questions

• How does Cas-protein length vary across different microbial lineages in GTDB?
• What is the effect of genome taxon on the likelihood of finding compact Cas effectors?
• Does the presence of unusual Cas gene neighborhoods predict a different protein architecture?
• To what extent do predicted protein sizes under 700 aa cluster in specific CRISPR subtypes?
• Which taxonomic groups contain the highest fraction of novel Cas-like architectures?
• How does ESMFold confidence compare between known Cas proteins and candidate compact effectors?

Basic Materials

• A computer with internet access and enough storage for genome files.
• Access to GTDB representative genome data.
• CRISPRCasFinder web access or local installation.
• A spreadsheet program such as Google Sheets or Excel.
• A text editor for notes and metadata.
• Python with Biopython for parsing genome and protein files.
• Basic reference material on CRISPR-Cas system classes and subtypes.

Advanced Materials

• A university or shared high-performance computing account.
• Local installations of CRISPRCasFinder, HMMER, and sequence annotation tools.
• Command-line Python environment with pandas, Biopython, and matplotlib.
• ESMFold access or a local protein-structure workflow.
• Protein family databases such as Pfam and NCBI Conserved Domain Database.
• Multiple sequence alignment tools such as MAFFT or MUSCLE.
• Phylogenetic analysis software such as IQ-TREE or RAxML-NG.

Software & Tools

• CRISPRCasFinder: Locates CRISPR arrays and nearby Cas genes in microbial genomes.
• GTDB: Provides standardized representative genomes for broad comparative searches.
• ESMFold: Predicts protein structure from amino acid sequence and helps you screen compact candidates.
• Python: Automates parsing, filtering, and summary statistics across many genomes.
• ImageJ: Measures features in exported figures if you need consistent image-based annotation.

Experiment Steps

1. Define your discovery rule set, including which genomes, Cas features, and protein-length cutoffs you will accept.
2. Build a genome-search workflow that finds CRISPR regions, extracts nearby Cas genes, and records metadata for each hit.
3. Classify each candidate into known or unusual architecture groups using domain annotation and sequence comparison.
4. Rank candidates by compactness, novelty, taxonomic spread, and structure-prediction confidence.
5. Check top hits against known databases and literature so you can separate real novelty from annotation noise.
6. Present your findings with a reproducible pipeline, summary tables, and a clear shortlist of follow-up candidates.

Common Pitfalls

• Mixing draft genomes with uneven annotation quality, which can create fake novel architectures.
• Treating every short Cas-like protein as a strong genome-editing candidate, which ignores weak domain evidence.
• Forgetting to separate duplicate hits from truly independent candidates, which inflates your counts.
• Comparing structure predictions without tracking confidence scores, which makes low-quality models look better than they are.
• Skipping taxonomy checks, which can make one oversampled clade look like a broad discovery.

What Makes This Competitive

A class-level version of this project just lists a few candidate proteins. A stronger version builds a clean pipeline, uses careful filters, and compares candidates across many microbial groups. You can push further by testing whether compact effectors cluster in certain lineages, domain families, or CRISPR subtypes. Strong statistical summaries and a reproducible shortlist matter more than a giant raw hit list.

Project Variations

• Focus only on archaeal genomes to see whether compact Cas effectors are more common in extreme environments.
• Compare CRISPR-Cas architecture diversity between host-associated microbes and free-living microbes.
• Add a protein-family analysis step to test whether compact candidates share hidden domain patterns with known editors.

Learn More

• NCBI Bookshelf: Search for free chapters on CRISPR-Cas biology and microbial defense systems.
• GTDB documentation: Read the database methods and taxonomy notes on the official GTDB site.
• PubMed: Search review articles on CRISPR-Cas system classification and compact effectors.
• NIH NCBI Resources: Explore genome, protein, and taxonomy databases linked from NCBI.
• Annual Review of Microbiology: Search for open abstracts and review articles on CRISPR evolution and diversity.
• ESMFold paper and documentation: Find the original paper and model notes through PubMed or the journal site.