This guide is designed for newcomers to microbial bioinformatics who want to understand the field before diving into complex analyses. Whether you’re a biology student, a wet-lab researcher transitioning to computational work, or someone curious about microbial communities, this introduction will help you build a solid foundation.
Learning Philosophy: We prioritize understanding concepts over rushing to results. Microbial bioinformatics combines biology, statistics, and computer science—taking time to understand each component will make you a better researcher.
Microbial bioinformatics is the application of computational methods to understand microbial life. This includes:
Microbes present unique challenges and opportunities:
Challenges:
Opportunities:
Key Insight: Microbial bioinformatics often deals with incomplete information. We’re constantly inferring what might be happening based on fragmentary data.
DNA → RNA → Protein → Function
In microbes:
Two fundamental questions in microbial bioinformatics:
“Who is there?” (Phylogeny)
“What are they doing?” (Function)
Important: Closely related microbes can have very different functions, and distantly related microbes can perform similar functions.
Understanding scale is crucial:
| Level | Method | Resolution | Example Question |
|---|---|---|---|
| Kingdom | 16S rRNA | Bacteria vs Archaea | Are these samples dominated by bacteria? |
| Phylum | 16S rRNA | Major groups | Are these gut or soil samples? |
| Genus | 16S rRNA | Close relatives | What Lactobacillus species are present? |
| Species/Strain | Whole genomes | Specific variants | Does this E. coli have antibiotic resistance? |
| Function | Gene content | Metabolic capacity | Can this community produce methane? |
Most of what we know about microbial diversity comes from DNA sequencing rather than growing cultures:
Amplicon Sequencing (16S, ITS):
Metagenomics:
Metatranscriptomics:
Command Line Proficiency
You’ll spend most of your time in terminal environments. Focus on:
- File navigation and manipulation
- Text processing (grep, awk, sed)
- File permissions and system administration basics
- Understanding pipes and redirection
Biology Background
Key concepts to understand:
- Central dogma (DNA → RNA → protein)
- Basic microbiology (cell structure, metabolism)
- Ecology principles (diversity, community structure)
- Evolution and phylogenetics
Statistics and Data Analysis
You don’t need to be a statistician, but understand:
- Descriptive statistics (mean, variance, distributions)
- Hypothesis testing basics
- Multiple testing corrections
- Data visualization principles
Check Your System:
# Verify you have a Unix-like environment
echo $SHELL
which bash
Essential Command Line Tools:
# These should be available on most systems
which grep awk sed sort uniq wc head tail
Package Managers:
FASTA Format (sequences):
>sequence_identifier description
ATCGATCGATCGATCG
GCTAGCTAGCTAGCTA
>another_sequence more_description
TTAATTAATTAATTAA
FASTQ Format (sequences with quality):
@sequence_identifier
ATCGATCGATCGATCG
+
IIIIIIIIIIIIIIII
GFF/GTF Format (genome annotations):
seqname source feature start end score strand frame attributes
chr1 RefSeq gene 1000 2000 . + . ID=gene1;Name=important_gene
Before any analysis, always check your data:
Questions to Ask:
Basic Commands:
# Count sequences in FASTA file
grep -c "^>" sequences.fasta
# Check sequence lengths
awk '/^>/ {print; getline; print length}' sequences.fasta | head -20
# Look at file structure
head -20 data.txt
tail -10 data.txt
wc -l data.txt
Purpose: Identify microbial community composition
Input: Paired-end FASTQ files from specific gene regions
Output: Taxa tables, diversity metrics, phylogenetic trees
Typical Workflow:
Purpose: Understand community function and composition
Input: Random DNA fragments from environmental samples
Output: Gene catalogs, functional profiles, genomic bins
Typical Workflow:
Purpose: Understand individual organism capabilities
Input: Pure culture genomic DNA
Output: Annotated genome, functional predictions
Typical Workflow:
Let’s work with a small dataset of bacterial 16S sequences:
# Download example data (simulated command - adapt for real data)
curl -O "https://raw.githubusercontent.com/CreeveyLab/Metataxonomics_Workshop/refs/heads/master/raw_seqs/hungate-16S.fas"
# Basic exploration
echo "Number of sequences:"
grep -c "^>" hungate-16S.fas
echo "First few sequence headers:"
grep "^>" hungate-16S.fas | head -5
echo "Sequence length distribution:"
awk '/^>/ {if (seq) print length(seq); seq=""} !/^>/ {seq=seq$0} END {print length(seq)}' hungate-16S.fas | sort -n | uniq -c
What to Look For:
# Extract sequences for comparison
# Get the first sequence
sed -n '1,2p' hungate-16S.fas > seq1.fasta
# Get the second sequence
sed -n '3,4p' hungate-16S.fas > seq2.fasta
# Simple comparison (count identical positions)
# This is a basic example - real tools are much more sophisticated
echo "Basic sequence comparison:"
paste <(grep -v ">" seq1.fasta | fold -w1) <(grep -v ">" seq2.fasta | fold -w1) | awk '$1==$2 {same++} END {print "Matching positions: " same}'
# Simulate working with a taxonomy file
echo "Creating example taxonomy data:"
cat > example_taxonomy.txt << EOF
OTU_001 Bacteria;Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacillus;acidophilus
OTU_002 Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae;Escherichia;coli
OTU_003 Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Blautia;obeum
EOF
# Parse taxonomic levels
echo "Phylum distribution:"
cut -f2 example_taxonomy.txt | cut -d';' -f2 | sort | uniq -c
echo "Genus distribution:"
cut -f2 example_taxonomy.txt | cut -d';' -f6 | sort | uniq -c
Key Learning Points:
Text Processing:
grep: Pattern searchingawk: Text processing and data extractionsed: Stream editingsort, uniq: Data organizationBioinformatics-Specific:
QIIME2 (Quantitative Insights Into Microbial Ecology):
Mothur:
R/Bioconductor:
Genome Assembly:
Functional Annotation:
Coursera: Bioinformatics Specialization
University of California San Diego
- Comprehensive introduction to bioinformatics
- Programming focus with biological applications
- Course Link
“Bioinformatics and Functional Genomics” by Jonathan Pevsner
“Unix and Perl to the Rescue!” by Keith Bradnam
“Statistical Analysis of Microbiome Data” by Xia, Sun, and Chen
Rosalind (rosalind.info):
Galaxy Training (training.galaxyproject.org):
QIIME2 Tutorials (docs.qiime2.org):
The Unix Shell (swcarpentry.github.io/shell-novice/):
Explain Shell (explainshell.com):
NCBI (ncbi.nlm.nih.gov):
Silva (arb-silva.de):
KEGG (kegg.jp):
Online Communities:
Professional Organizations:
Journals to Follow:
Podcasts:
Microbial bioinformatics is a rapidly evolving field that combines multiple disciplines. Success comes from:
Building Strong Foundations: Take time to understand biology, statistics, and computing fundamentals
Learning Continuously: New methods and tools appear regularly—stay curious and keep learning
Connecting with Community: Science is collaborative—engage with others, ask questions, share knowledge
Starting Small: Begin with simple questions and datasets—complexity will come naturally with experience
Remember: every expert was once a beginner. The field is welcoming to newcomers who show curiosity and dedication. Focus on understanding concepts rather than rushing to advanced techniques, and don’t be afraid to ask questions.
Welcome to the fascinating world of microbial bioinformatics!
This guide provides a foundation for learning microbial bioinformatics. As you progress, you’ll discover specialized areas that match your interests and research goals. Keep this guide as a reference and update it based on your learning journey.