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What does the term 'bioinformatics' refer to?

Bioinformatics is a field that involves analyzing biological data, extracting patterns, and generating hypotheses based on those patterns.

What are the primary activities involved in bioinformatics? (Choose all that apply.)

  • Development of new pharmaceuticals
  • Integration of diverse types of data (correct)
  • Clinical trial data analysis
  • Storage, search, and retrieval of data (correct)

    • Which of the following types of data are NOT a type of data commonly used in bioinformatics? (Choose all that apply.)

  • Image data (static or video)
  • Numeric data
  • Audio data
  • Text data
  • Meteorological data (correct)

    • What is meant by 'high throughput data' in the context of Biology?

    <p>High throughput data refers to the generation of massive amounts of biological data, often at a rapid pace, thanks to technological advancements and automation.</p> Signup and view all the answers

    Which of these advancements are contributing factors to the rise of high throughput data in Biology? (Choose all that apply.)

    <p>Algorithms</p> Signup and view all the answers

    What is a typical example of a high-throughput data generation activity?

    <p>Genome sequencing</p> Signup and view all the answers

    The sequence of letters in DNA resembles a similar concept to the sequence of letters in a written language.

    <p>True</p> Signup and view all the answers

    What is 'DNA or whole genome sequencing'?

    <p>DNA or whole genome sequencing refers to the process of determining the order of nucleotides in a DNA molecule.</p> Signup and view all the answers

    What is Moore's Law, and why is it relevant in the context of DNA sequencing cost reduction?

    <p>Moore's Law states that the number of transistors on a microchip doubles approximately every two years. This rapid increase in computing power contributes to the decreasing cost of DNA sequencing.</p> Signup and view all the answers

    What led to a significant decrease in the cost of DNA sequencing around 2008?

    <p>The introduction of ‘Next Generation Sequencing’ (NGS) technologies</p> Signup and view all the answers

    How large is the amount of data generated in genomics compared to other fields, such as astronomy, Twitter or YouTube?

    <p>The amount of data generated in genomics is substantial, often exceeding the size of data produced in fields like astronomy, Twitter, or YouTube.</p> Signup and view all the answers

    How has the time and cost associated with sequencing a human genome changed over time?

    <p>The time and cost associated with sequencing a human genome have dramatically decreased since the initial efforts in the 1990s. Now, it can be done in a matter of hours at a significantly lower cost.</p> Signup and view all the answers

    Genomics is the only area of biology that generates 'omics data'.

    <p>False</p> Signup and view all the answers

    What are 'omes' and '-omics' in the context of biological research?

    <p>'Omes' refer to the complete set of a particular type of molecule, like the genome (all DNA) or proteome (all proteins). '-omics' refers to the study of these complete sets.</p> Signup and view all the answers

    Which of the following is NOT an example of an 'ome'?

    <p>Biome</p> Signup and view all the answers

    What does Sydney Brenner's quote, "Drowning in a sea of data and starving for knowledge" highlight?

    <p>The challenges of interpreting data in a meaningful way to gain insights</p> Signup and view all the answers

    What is Sir Paul Nurse's perspective on the role of data generation in scientific research?

    <p>Sir Paul Nurse believes that data should not be an end in itself, but a means to gaining knowledge. He emphasizes that scientific research also requires the generation of ideas.</p> Signup and view all the answers

    Bioinformatics is a highly interdisciplinary field that combines various scientific disciplines.

    <p>True</p> Signup and view all the answers

    What is the key role of 'domain knowledge’ in bioinformatics?

    <p>Providing context and interpretation to the analyzed data</p> Signup and view all the answers

    What is the difference between 'explainable methods' and 'interpretable models' in the context of AI and ML?

    <p>Explainable methods help us understand the reasoning behind a model's prediction after the fact, while interpretable models are designed to be transparent in their structure and decision-making process.</p> Signup and view all the answers

    Study Notes

    Bioinformatics

    • Bioinformatics is a broad, inclusive field
    • Analyze biological data to find patterns and generate hypotheses
    • Activities include:
      • Storage, searching, and retrieving data
      • Creating thematic databases (primary and/or derived data)
      • Integrating diverse data types

    Learning Objectives

    • Big data in biology: where does data come from?
    • Data generation: is it the end or a means?
    • Making sense of data
    • Four illustrative examples
    • Concerns

    Types of Data

    • Image data (static or video): examples include T2*-weighted, Diffusion-weighted, T1-weighted images
    • Audio data: examples include elephant calls, plant sounds, bird and insect chirps
    • Text data: a variety of formats, including spatial transcriptomics techniques and gene expression data.
    • Numerical data: examples include expression data sets, possibly from TCGA genetic data.

    Where are we getting data from? / How are we getting data?

    • This question prompts discussion of the origins of large biological datasets.

    Throughput (from Lecture 13)

    • Output per unit time
    • Example: 2,000 chapatis per hour

    High Throughput Data in Biology

    • A consequence of advancements in multiple areas
    • Includes algorithms, chemistry, computer hardware, cross-disciplinary studies, genetic engineering, instrumentation, microscopy, software, and spectroscopy

    Genome Sequencing

    • A typical high-throughput data generation activity

    Text and DNA

    • Analogies between text and DNA illustrating their similar sequence-based nature.
    • Data content depends on the order of letters (nucleotides).
    • Emphasis on the importance of sequence in both DNA and text

    DNA Double Helix Features

    • The two DNA strands are antiparallel
    • Has specific end points (5' and 3')

    DNA or Whole Genome Sequencing

    • The order of nucleotides (A, C, G, and T) in DNA.

    Cost of Sequencing

    • Cost of sequencing (USD per Megabase of DNA) has fallen dramatically through technological advancements (Moore's Law) since 2003.
      • Moore's law describes the trend of doubling computer power roughly every two years.
      • A "Next Generation Sequencing" (NGS) technology development around 2008 contributed significantly to this reduction.

    How Big is Genomics Data? (2015)

    • Data phase: a comparison of data sizes across fields (Astronomy, Twitter, YouTube, Genomics) by 2015.
      • Genomics data is very large.

    Genome Analysis (Then vs. Now)

    • Cost of sequencing is significantly lower now than in the past
    • Number of laboratories performing genome studies has declined greatly
    • Speed of sequencing has greatly increased.

    Is Genomics the Only -Omics Generating Data?

    • No. There are many other -omics.

    -Omics and -omes

    • There are many -omes and -omics
    • The genome is static
    • Other -omes are spatiotemporally dynamic and condition-dependent

    Mitochondrial Variations

    • Descriptions of different types of mitochondrial structures
    • "Low" energy vs. "high" energy states of mitochondria.

    A Few -Omics (Other Than Genome)

    • Several types of data besides genomes are considered here.
      • Proteome
      • Microbiome
      • Glycome
      • Metabolome
      • Epigenome
      • Transcriptome
      • Lipidome

    Summary So Far...

    • Data related to biology is generated in vast amounts
    • A consequence of high-throughput -omics studies.
    • Advancement in various domains has driven this.
    • Data generation has become more affordable

    Data Generation: End or Means?

    • A question about the purpose of generating data and if it's an end goal or an intermediary process.

    Does Data Mean Knowledge?

    • Question about whether large amounts of data equate to true understanding.
    • Quotation from Sydney Brenner (Nobel Prize winner) on the distinction between data and knowledge.

    Converting Data Into Knowledge

    • Observations, questions, hypotheses, experiments, and predictions are essential components of the scientific process.
    • This is a reiterative process not just data collection.

    Inter-connected + Inter-related -Omics

    • Big data and art are linked in this slide in the visual.

    Bioinformatics--Making Sense of Data

    • Understanding large datasets requires specific techniques.

    Bioinformatics--A Multi-disciplinary Field

    • Bioinformatics is built on concepts from biology, statistics, and computer science.

    Statistics and Machine Learning Algorithms

    • Traditional methods may assume normal data distribution
    • New methods need to be developed to handle diverse biological data.

    Data Science Analysis Stack

    • A layered approach to analyzing biological data, highlighting various stages and components.

    Where Does Domain Knowledge Come In?

    • Shows a layered approach highlighting the role of different kinds of knowledge throughout the data analysis process.

    Biologists' Viewpoint (the Scientific Method)

    • The scientific method is shown as a cyclical process
    • Beginning with observation and knowledge to generate questions, form hypotheses, and making predictions; cyclical process.

    Summary So Far (Another summary)

    • High-throughput -omics produces large datasets
    • Making sense requires domain knowledge, advanced statistical methods (AI and ML), and computer science advances (storage, search, etc)

    Illustrative Examples: 1 of 4

    • Use the image data and linear support vector machine (SVM) as an illustration

    A Brain Disorder

    • Normal Pressure Hydrocephalus (NPH): a brain condition with symptoms.
    • Accumulation of cerebrospinal fluid (CSF) in the brain cavities, but without high pressure

    Problem Statement

    • 3D MRI images may provide early signs of NPH before clinical symptoms arise.
    • Early detection allows better treatment and management.
    • Machine learning can aid in this process by assisting diagnosis, helping manage patients.

    3D MRI Images

    • Description of 3D MRI images of the brain for NPH and related data interpretation

    Input Data

    • Input data is 3D MRI images; positive (affected individuals) and negative (not affected individuals)

    Data Analysis

    • Data is used to train a machine learning (ML) algorithm
    • This algorithm can classify new, unseen images
    • Specific training requirements and skill sets are necessary to accomplish this

    Outcome

    • The performance of the ML algorithm is compared to senior and junior medical doctors.
    • The performance appears comparable.

    Illustrative Example 2 of 4

    • Type of data: Text
    • Algorithm: Profile hidden Markov models (HMMs)
    • Myoglobin, hemoglobin, and their related forms of hemoglobin are discussed in terms of their evolution.

    Problem Statement

    • Problem statement regarding protein biochemistry and the implications of different amino acid sequences.
    • Specifically focusing on myoglobin, hemoglobin alpha, beta, and gamma subunits.

    Approach: Gather Data

    • Collect sequences of globins from diverse species (myoglobin and different hemoglobins)

    Sequence Comparisons

    • Comparisons between sequences to identify conserved elements and differences

    Outcome

    • Focus on the positions in the sequence that are universal to variations of these proteins.
    • Discussion of why myoglobin is monomeric, and why other forms are in oligomer forms.
    • Explanation of the differences in oxygen saturation curves.

    Illustrative Example 3 of 4

    • Type of data: Text
    • End goal is hypothesis generation
    • Algorithm: Large language model( LLMs)

    Background Leading to the Objective

    • The objective is to find the function of an orphan protein
    • DNA sequencing is the basis for the exploration.

    One of the Ways to Predict Function is...

    • Illustrates an approach to find function by identifying patterns associated with similar biological proteins.

    Gathering Data for “Learning”

    • Gathering data using biological literature and databases
    • Identifying patterns to assign function.

    Problem Statement (another problem statement)

    • The task is finding the function of some unknown or uncharacterized proteins.
    • The information required is taken from research and medical literature.

    Solution

    • This problem is solved by training a large language model to read and summarize countless research documents; then assign function or identify patterns
    • There is a significant reduction in the time needed to accomplish this compared to a human's workload.

    Illustrative Example 4 of 4

    • Type of data: numeric + text
    • End goal: societal benefit
    • Algorithm: statistical tests of significance

    Background Leading to the Objective (another background)

    • Problem of diagnosing a pelvic mass
    • Diagnosis depends on correctly identifying cancerous vs. non-cancerous occurrences.
    • Identifying experts in the field is difficult.

    Question

    • Need a test to identify a cancerous pelvic mass
    • High sensitivity (missing no cancerous occurrences/instances)
    • High specificity (correctly identify/diagnose only cancerous instances)
    • Proteomics is the approach.

    Solution

    • Development of a multivariate index based on biomarkers using proteomics studies.
    • The use of serum levels of five proteins as biomarkers.

    Concerns

    • Issues with the reproducibility of scientific studies.
    • Inconsistencies and insufficient details across published research can create difficulties in replicating and validating results.

    Output: Trust Blindly or With Caution?

    • Output quality depends on the input data
    • Quality control is essential to ensure reliable results
    • Input that is not well curated may yield unreliable results.

    AI & ML Algorithms Are Quite Powerful

    • Describes an algorithm metaphorically as a black box
    • The internal workings are often complicated and not readily transparent

    Ask Questions About Output

    • Importance of critical examination of prediction results.

    A Tethered Cow

    • An analogy used to illustrate structure of a protein
    • Different structural domains are analogous to parts of the cow and/or the tether

    Architecture of a Protein

    • Showing the components of a protein: the flexible tether, anchoring domain, and catalytic domain
    • Identifying patterns within protein structure/sequences
    • Finding patterns in the protein sequence can relate to function.

    AI & ML: Explain and Interpret Results

    • Methods to explain or interpret the outputs from AI/ML are needed
    • A crucial step for understanding the reasoning/basis of predictions made by these algorithms

    Gut Feeling/Intuition of Algorithms

    • The significance of intuition or gut feeling in making decisions
    • There is a value and importance to this type of thought process also.

    Errors in Databases and Propagation

    • Errors in biological databases are a concern
    • Care should be taken when using data from these sources
    • Data consistency and reliability are critical.

    Clues to Questions at the Level of...

    • Diagram showing the different levels of biological organization (from ecosystem to molecules).

    Water, Water, Everywhere...

    • Title
    • No other relevant information provided.

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