Introduction to Bioinformatics

Introduction to Bioinformatics
A tale of myths and legends
[Freevector]
June 16, 2011

June 16, 2011
“Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline.”
National Center for Biotechnology Information
(NCBI)

Areas where bioinformatics is applied
Genomics
Genomic feature prediction
Sequencing data analysis
Proteomics
Protein 3D structure modeling
Drug design
Systems Biology
Gene set enrichment
Pathway analysis
Phenotype
Image analysis
Integration
June 16, 2011

Approach
Biological Question
Generate Data
Translate into a computer solvable task
Develop an algorithm
Implement algorithm
Run algorithm
Condense result in human readable form
Answer Biological Question
Example
Genes regulated by protein X
ChIP-Seq data
“Align reads and identify clusters in the genome”
Choose data structures
Write source code
Align reads
Write script to summarize results genome wide
Report protein’s binding sites
June 16, 2011
“Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline.”
NCBI

The challenges in bioinformatics
Acceptance by biological collaborators when all that matters for the publication is the biology
Retaining quality work
Workflows poorly annotated in papers
Programs poorly written
No reproducibility
Keeping up-to-date
New programs are published every week
New formats because no time to evaluate existing standards
New databases because existing ones full of noise
June 16, 2011

Bioinformatics a mythical creature?
June 16, 2011
Christos Ouzounis
Head of the Computational Genomics Group at the
European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Cambridge UK

Myth #1: Anybody can do it!
Assumption: Most bioinformatics analysis can be done by using web applications and commercial programs with GUIs
June 16, 2011
http://www.broadinstitute.org/cancer/software/genepattern/index.html
http://main.g2.bx.psu.edu/
Ouzounis C. Two or three myths about bioinformatics. Bioinformatics. 2000
Mar;16(3):187-9. PubMed PMID: 10869011.

Customized answers require expert input
Anyone can do predefined analysis with web pages and off-the-shelf programs, however
Using tools without understanding the methodology is dangerous
“Anyone” needs to understand algorithmic papers
E.g. Program might produce output that has a certain bias, not knowing this the researchers could publish this artificial bias as biological result.
A standard bioinformatics tool that works well for general tasks does not exist
E.g. Local-Alignment Algorithm (1981) vs. PCR (1983) in NGS
Only novel tools/pipelines can provide customized answers
“Anyone” needs to be proficient in programming to write the required algorithms/scripts
E.g. You might have to settle for comparing your features with known genes because the program is not able to compare to novel transcripts
June 16, 2011
!
Smith, Temple F.; and Waterman, Michael S. (1981). "Identification of Common Molecular Subsequences". Journal of Molecular Biology 147: 195–197.

Myth #2: Bioinformatics is a service
Assumption: Bioinformatics merely supports the experimental research and can be a disconnect service
June 16, 2011
Traditional Biology
Hypothesis
Experiment
Eyeballing
Experimental Design
Evaluation
Biology
High Throughput Biology (assumption)
Experiment
Biology
Hypothesis
Experimental Design
Evaluation
Data analysis
Bioinformatics

Interdisciplinary analysis requires an interdisciplinary team throughout
!
June 16, 2011
Standard data analysis can be a service task, however
Having a service performed without knowing the methodology is dangerous.
“Service” needs to make scientific decisions to take the assumptions under which the data was produced into account.
Repeating a statistical test for all genes requires an E-value to be calculated.
Producing data not suitable for the planned analysis is wasteful.
“Service” needs to have scientific input in the experimental design to ensure the data can be analyzed.
Comparing the distribution of mapped reads of runs with different read lengths will result in a difference that is due to the mapping bias of different read lengths.
High Throughput Biology
Experiment
Analysis
Evaluation
Experimental Design
Hypothesis

Myth #4: Bioinformatics is quick
Assumption: bioinformatics analysis can be done quickly because computers are involved.
June 16, 2011
http://www.ads-links.com/images/wp/keyboard-fast.gif

Bioinformatics analysis is a scientific experiment in itself
Bioinformatics is faster than manual work, however
Quick tasks accumulated take a long time
Task: Map 15 million reads of 76 bp length against the complete human genome (hg18)
Manual: couple of decades
Brute-Force: couple of years
BLAST (1995): couple of days
Modern Aligners: BWA ~ 4 h
Bioinformatics is a proper scientific experiment in itself requires time for experimental design, development of controls, parameter tuning, evaluation, and summarizing.
June 16, 2011
!
Bao S, Jiang R, Kwan W, Wang B, Ma X, Song YQ. Evaluation of next-generation sequencing software in mapping and assembly. J Hum Genet. 2011 Apr 28. PubMed PMID: 21525877.

Myth #5: All dry-lab research does the same
Assumption: bioinformatics is interchangeable with other dry lab research area because they all “analyze data”.
Assumption: All biological research areas are interchangeable because they all “work with samples”.
June 16, 2011

Three things to remember
Bioinformatics requires dedication and continuity
Bioinformatics data analysis is a full research experiment in itself
We get the most out of our research if we work as a interdisciplinary research team throughout
June 16, 2011
Experiment
Analysis
Evaluation
Experimental Design
Hypothesis

Next week:
June 16, 2011
Abstract: An introduction to second generation sequencing will be given with focus on the production informatics: The basic approach of read-mapping and feature extraction will be introduced and challenges associated with sequencing errors discussed.
http://web.qbi.uq.edu.au/labs/gseq/analysis/bioinformatics-seminar-series/

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June 16, 2011

Introduction to Bioinformatics

by Denis Bauer on Jun 15, 2011

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