Bioinformatics and Functional Genomics — Introductory Notes (UBSCRI-302)

Acknowledgement and Course Context

  • Traditional custodians acknowledged: Auobacal people.
  • Course: UBSCRI-302 Bioinformatics and Functional Genomics.
  • Structure: two hours per week of lectures and two hours of related computer sessions (Prax). Total: 24 hours of lectures and 20 hours of Prax.
  • Expect a cultural and cognitive shift: this is many students’ first exposure to bioinformatics; be prepared for initial shock, difficulty, and stress, followed by familiarity.
  • Bioinformatics is broadly useful across biology, medicine, and industry; it’s becoming essential in all areas of biology and medicine.
  • Comparison imagery: computational biologists face less lab safety/ethics pressure than experimental biologists; this is an additional career path, not a replacement.
  • Instructor background: PhD in biochemistry (France); postdoc in biochemistry (UC Irvine); focus on functional genomics and cancer; bioinformatics applied to cancer but applicable across biology and biomedicine.
  • Office and contact: Life Science Building, 3rd Floor; best via email to ensure availability.

What is Bioinformatics?

  • Three dictionary-style definitions:
    • Bioinformatics is the unified discipline from the combination of biology, computer science and information technology.\text{Bioinformatics is the unified discipline from the combination of biology, computer science and information technology.}
    • Mathematical, statistical and computing methods to solve biological problems using DNA and amino acid sequences and related information.\text{Mathematical, statistical and computing methods to solve biological problems using DNA and amino acid sequences and related information.}
    • Informatics for biology; computer science for biology.\text{Informatics for biology; computer science for biology.}
  • Core idea: use computers to store, analyze and manipulate biological information; can be used ethically to advance biology, not for harmful purposes.
  • Course design note: not tailored to computer experts; no prior advanced CS knowledge required beyond everyday computing; emphasis on practice and application.

Functional Genomics: Core Concepts

  • Functional genomics levels (biochemical entities):
    • DNA (genome) → RNA (transcriptome) → Proteins (proteome) → Metabolites (metabolome).
  • Definitions:
    • Genomics: study of the genome; structure, variation, evolution.
    • Transcriptomics: study of the transcriptome (RNA transcripts).
    • Proteomics: study of the proteome (proteins) and their properties.
    • Metabolomics: study of metabolites (small molecules).
  • Functional genomics today includes genomics, transcriptomics, and proteomics; metabolomics is increasingly integrated and may become part of functional genomics.
  • Distinction between levels:
    • Genome (DNA) is relatively stable across time and cell types; changes are rare (methylation, mutations).
    • Transcriptome and proteome are highly dynamic and cell-type- and environment-dependent.
  • Context: the course focuses on bioinformatics for genomics, transcriptomics and proteomics in biomedical science; metabolomics may be included as it expands relevance.

The Human Genome Project and Genome Basics

  • Human genome sequencing journey:
    • Initiated in the 1990s as an international project; competed by public (HUGO) and private (Celera) initiatives.
    • Draft completed around 2000; a refined draft/public release around 02/2003; not all details fully resolved even today.
  • Sources and samples:
    • Human genome sequence derived from blood white blood cells and sperm cells; red blood cells lack a nucleus in humans.
    • Donors from multiple populations worldwide; results are a mosaic of international contributions, not a single individual’s genome.
  • Costs and accessibility:
    • Historically huge cost; today, whole-genome sequencing can be around $1,000\$1{,}000 per genome in some services.
  • Key statistics:
    • ≈2.0×104\approx 2.0\times 10^4 protein-coding genes (often cited as 20,000) in the human genome.
    • Coding DNA represents about 1.5%1.5\% of the genome; exons constitute about 25%25\% of the coding regions.
    • Much of the remainder includes introns, regulatory elements, and transposable elements.
    • Non-coding RNAs constitute a large portion of transcripts; estimates suggest roughly 105 to 2.5×10510^5 \text{ to } 2.5\times 10^5 distinct mRNA transcripts, though only a portion are protein-coding.
  • Important caveats:
    • Even the established numbers are estimates; different methods yield varying figures (e.g., total RNAs, transcripts, and proteins).
    • The genome is not fully understood; many regulatory elements and non-coding RNAs remain to be characterized.
  • Patent and commercial aspects:
    • Human genes, RNA, and proteins cannot be patented under then-current laws; some biomarker-based uses may be patentable, but the fundamental genes/proteins themselves are not.
    • Private funding and commercial interest existed, with speculation that sequencing would immediately translate to disease understanding and therapies (not fully realized due to disease complexity being multi-factorial).
  • Key takeaways:
    • The genome is stable across cells, but the transcriptome and proteome are dynamic and context-dependent.
    • Genomics, transcriptomics and proteomics expand the complexity from the genome to downstream layers via alternative processes.
  • Notable concept: the size and complexity of genomes across life forms vary dramatically; plants often have much larger genomes than humans, illustrating that larger genomes do not always map to greater organismal complexity.

From Genome to Transcriptome to Proteome: Complexity and Measurement

  • From genes to transcripts to proteins:
    • Gene count: ≈2×104\approx 2\times 10^4.
    • Transcript count: ∼105 to 2.5×105\sim 10^5 \text{ to } 2.5\times 10^5 (due to alternative splicing, RNA editing, and alternative promoters).
    • Protein count (including PTMs and isoforms): ≈106\approx 10^6 proteins.
  • Reasons for the jump in numbers:
    • Alternative splicing: multiple mRNA variants per gene.
    • Post-translational modifications (PTMs): generate multiple functional protein forms from a single gene product.
    • Protein-protein interactions and structural conformations influence function beyond simple sequence complementarity.
  • Key contrasts:
    • Nucleic acids: 5 nucleotides (A, T, C, G; U in RNA);
    • Proteins: 20 amino acids; complexity increased by 200+ PTMs and by alternative splicing.
  • Protein interactions vs nucleic acid base pairing:
    • Nucleic acids: recognition often via complementary sequences.
    • Proteins: recognition often via 3D shape and conformational compatibility, not straightforward sequence complementarity.
  • Genomic localization:
    • DNA largely in nucleus and mitochondria; proteins distributed throughout all cellular compartments.
  • Amplification:
    • Nucleic acids can be amplified by PCR; proteins cannot be amplified in the same way.
  • Systems biology:
    • Integration of genomics, transcriptomics, proteomics with cell biology and physiology to understand the system as a whole.

Databases and Tools: Where Data Live

  • Big data and public databases:
    • Public funding requirements ensure broad access; data are dynamic and constantly updated.
    • Databases store genome sequences, transcript data, protein sequences, expression data, and disease associations.
    • Data are evolving; mistakes exist and are corrected over time; data entries change day to day.
  • Key databases and tools to be used in this course:
    • NCBI (National Center for Biotechnology Information): core resource for DNA, RNA, gene expression, disease associations, protein sequences, taxonomy, and cross-references.
    • BLAST: a tool within NCBI for sequence similarity comparisons across genes, transcripts, diseases, and organisms.
    • UniProt: central proteomics database (Swiss-based) for protein sequences and annotations; integrates with NCBI.
    • Human Protein Atlas: localization of proteins in tissues; distribution and expression across tissues using publications and immunohistochemistry data.
    • DAVID: functional classification and pathway analysis; interactome and protein interactions.
  • Data integration and publication links:
    • Publications and datasets linked in the databases; most public research is discoverable here.
  • Privacy and access considerations:
    • Private company data may not be public; otherwise, data are publicly accessible.
  • Practical note for Prax sessions:
    • First Prax will focus on genomics using NCBI and BLAST; subsequent Prax covers proteomics with UniProt and Human Protein Atlas; integration with DAVID for functional interpretation.
  • The dynamic nature of databases:
    • Entries, tools, and interfaces change over time; students should focus on underlying principles rather than a single interface.

Course Structure, Assessment, and Study Approach

  • Learning activities:
    • Two-part structure: before midterm (bioinformatics for genomics) and after midterm (bioinformatics for proteomics).
    • Two hours of lectures per week plus two hours of Prax each week (on Zoom). Prax start this week; two groups: Group 1 (09:00–11:00) and Group 2 (13:00–15:00).
  • Midterm and final assessments:
    • Practical assessments (Prax): Midterm practical 20%, Final practical 20% (online).
    • Theory assessments: Midterm theory (paper-based) 30%, Final theory (online, two hours) 30%.
    • Total: 40% Prax, 60% theory; compulsory to attempt both computer lab exams to pass.
    • Midterm computer lab exam is during Week 7 (Sept 9, online for prac) and covers genomics.
    • Final computer lab exam (post-midterm) covers proteomics; similar online format in Week 12.
    • Exam times: two groups for Prax, but only one group for the online exam; notification will specify times.
  • Exam and practice alignment:
    • The first lectures align with the first Prax (genomics); subsequent weeks align with later Prax sessions; sometimes Prax occurs before theory, which is intentional to reinforce learning.
  • Study time and workload:
    • Course outline suggests ~140140 hours of total effort for the semester (10 UoS). This implies an average of ~4−54-5 hours per week for study, though individual needs vary widely.
  • Reading and resources:
    • Recommended core text: "Bioinformatics and Functional Genomics" (library copy; comprehensive but not highly practical).
    • Many students prefer smaller, practical books or online resources due to rapid changes in the field.
    • NIH educational resources (e.g., NIH/NHGRI genomics education pages) provide up-to-date background on genomics and proteomics; a valuable online resource for foundational knowledge.
    • Books are not strictly necessary; much information is available online and updated frequently.
  • Practical and exam tips:
    • The exam will mix MCQs and short-answer questions; emphasis will be on understanding and application, not memorization alone.
    • Practice and understanding are more valuable than rote memorization or MCQ tricks.
    • An example MCQ discussed: bioinformatics is used in genomics, transcriptomics, and proteomics (best answer: D, but C is sometimes acceptable since it’s course-related).
    • Short answer example: explain how 20,000 genes can yield 100,000 to 250,000 transcripts and about 1,000,000 proteins. Answer: alternative splicing plus mRNA editing and alternative promoters (and PTMs for proteins).
  • Practical study advice:
    • Learn by doing; you can start with Prax to see how the databases work, then deepen theory as needed.
    • The professor emphasizes enjoying the learning process and focusing on practical skills rather than exam anxiety.
  • Adverse circumstances and communication:
    • Circumstances affecting performance must be justified via online submission with documentation; discuss concerns with the course coordinator before applying.
  • Course logistics reminders:
    • Five-minute break after the introduction; Wednesday online session via Zoom (Canvas links).
    • The exam schedule and platform will be announced; ensure you have a functioning internet connection for Zoom Prax.

Practical Insights and Real-World Context

  • Practical import of bioinformatics:
    • Bioinformatics is essential for most modern biomedical tasks, including teaching, research, clinical practice, and industry.
    • Even non-research roles (e.g., pharmaceutical sales) increasingly rely on bioinformatics software and databases to understand products and data.
  • Ethical and scientific humility:
    • Emphasis on uncertainty in biology and genomics; not all questions have definitive answers; statistics and probability underpin many claims.
    • Be cautious about overconfidence regarding genome annotations and disease associations; many findings are hypotheses awaiting validation.
  • Metaphor and mindset:
    • Learning bioinformatics is like learning to ride a bike: practice first, theory later; understanding comes with doing rather than memorizing.
  • Practical examples and context mentioned in lectures:
    • The insulin example illustrates tissue distribution knowledge captured in the Human Protein Atlas.
    • The non-human elements (plants with large genomes) illustrate genome size vs organismal complexity slippage.
    • The shift from a purely genome-centric view to an integrative view (genome, transcriptome, proteome) reflects modern biology.

Quick Exam-Style Questions (Illustrative)

  • MCQ example discussed in lecture:
    • Bioinformatics is used in which areas?
    • A) Genomics only
    • B) Genomics and transcriptomics
    • C) Genomics, transcriptomics, and proteomics
    • D) Genomics, transcriptomics, proteomics, and beyond
    • Correct: D (though C is acceptable in context)
  • Short answer example discussed:
    • How do you explain the transition from ~2×1042\times 10^4 genes to ~105 to 2.5×10510^5\text{ to }2.5\times 10^5 transcripts and ~10610^6 proteins?
    • Answer (concise): Alternative splicing increases transcript diversity; RNA editing and alternative promoters add transcripts; PTMs and alternative splicing of proteins add to the proteome count.
  • Short answer exam tip:
    • The time allotted (e.g., 15 minutes) reflects thinking time and not the page length; answers may be brief if they are accurate and complete.

Practical Resources and Next Steps

  • Video and online resources to review before next lecture:
    • Introduction to the Human Genome
    • Introduction to Bioinformatics
    • Bioinformatics and Biological Databases
  • Key online resources to explore during Prax:
    • NCBI resources and BLAST practice
    • UniProt for protein sequences and annotations
    • Human Protein Atlas for tissue-level expression data
    • DAVID for functional classification and pathway analysis
  • Suggested reading and exploration:
    • NIH/NHGRI educational pages on genomics and proteomics for foundational knowledge
    • Optional textbook: Bioinformatics and Functional Genomics (for broad background; not strictly required due to rapid updates online)

Quick Reminders from the Instructor

  • Do not fixate on memorization; aim to understand concepts and be able to apply tools to real-world biomedical problems.
  • Expect continuous updates to databases and tools; focus on understanding procedures and how to interpret outputs rather than memorizing exact data points.
  • If you have questions, contact the course coordinator; do not hesitate to discuss issues early.

Summary of Key Takeaways

  • Bioinformatics integrates biology and computation to analyze large-scale biological data (genomics, transcriptomics, proteomics).
  • The genome provides the stable blueprint; the transcriptome and proteome reflect dynamic expression and regulation.
  • The Human Genome Project transformed biology but revealed that many questions remain due to non-coding elements, regulatory RNAs, and proteome complexity.
  • Data resources (NCBI, BLAST, UniProt, Human Protein Atlas, DAVID) are publicly accessible and continually updated; practice is essential to become proficient.
  • Course structure emphasizes practice (Prax) alongside lectures; success relies on consistent work and integrating theory with hands-on experience.
  • Exam format is mixed, with emphasis on understanding and applying concepts rather than rote memorization; plan for both MCQs and short answers.
  • Ethical, practical, and professional context: data accessibility, changing databases, and uncertainties in biology require a thoughtful, critical approach to bioinformatics.