BT201 Official Handouts

Biology and Informational Science

 The HGP changed how we view & practice biology.

 Biology is an informational science.

✓ Digital genome

✓ Environmental signals

 Biology has become a cross-disciplinary science.

Bioinformatics and intersecting discipline

Mathematical sciences Computer science Life sciences Developing the high throughput technologies and computational/mathematical tools required for this new biology

Why? Where? What? How?

Why: Ideas for what to produce these huge datasets? Biological background needed. Where: Raw data need to store, IT platforms required.

What: Patterns in datasets that can be analyzed using computers. Various data models and their respective algorithms are needed.

How: Different resources need to be integrated.

What is Bioinformatics?

The field of biology specializing in developing hardware and software to store and analyze the huge amounts of data being generated by life scientists. (NIH). More than 20 different definitions can be found from Google!

Computational Biology?

Computational Molecular Biology?

Data integration

Various molecular biology databases

Bioinformatics applications

Data analysis

Key Challenge of Bioinformatics

The world of biology is very different from what it was even ten years ago. To bridge the considerable gap between technical data production and its use by scientists for biological discovery.

Gene discovery

(Genome analysis)

Gene Discovery

In a fully sequenced genome, genes are found by scanning the sequence using gene-predicting computer programmes and assigning putative functions by searching for similarities in already existing databases. For many organisms under investigation in ecological genomics, no genomic database is available and genes must be identified in other ways. There are some so-called pregenomic molecular approaches that may be used to identify ecologically important genes in incompletely characterized genomes. In some cases the primary structure of a gene product (a protein) may be the starting point of gene discovery. This holds especially for proteins that can be isolated relatively easily by some marker or bioassay, or proteins that are highly induced by some experimental treatment. As an example we discuss the isolation of the metallothionein (mt) gene in a species of springtail, orchesella sancta (hens Bergen et al. 1999). Attempts to pick up the gene by polymerase chain reaction (PCR) using primers from the then-known drosophila mt sequence remained unsuccessful.

Genomics Revolution Invading Ecology

the suffix -ome :the collectivity of units (Lederberg and McCray 2001), example in coelome, the system of body cavities, biome, the entire community of plants and animals in a climatic region. In aiming to investigate many genes at the same time genomics differs from ecology, which although investigating many phenotypes, usually deals with only a few genes at a time.

The playing field of ecological genomics

In between genomics, with its focus on the single genome of a model organism, studying all the genes that it contains, and ecology, studying a few genes in many species.

The interactions between the genes within the · genome and the dynamic character of the · genome

sketched by Dover (1999) as an internal tangled bank.

idea of Darwin (1859)

Dover (1999 :

• ‘external tangled bank’ (the ecology)
• the ‘internal tangled bank’ (the genome)

The internal tangled bank

It emphasizes the role of genetic turbulence (gene duplication, genetic sweeps, exon shuffling, transposition, etc.) illustrates that there is ample scope for ‘innovation from within genetic turbulence

The genomics revolution

Three major technological developments of 1990s

microtechnology computing communication.

Microecology. The possibility of working with molecules on the scale of a few micrometers, given by advances in laser technology, has been very important for one of genomics’ most conspicuous achievements, the development of the gene chip.

Three major technological developments

Computing technology

To assemble a genome from a series of sequences requires tremendous computational power. Extensive calculations are also necessary for the analysis of expression matrices and protein databases. Without the advent of high-speed computers and data-storage systems of vast capacity all this would have been impossible. Consulting genome databases all over the world has become such normal practice that the scientific progress of any genomics laboratory has become completely dependent on communication with the genomics is that it is the study of the genome and its products as a unitary whole.

Schematic platform for Bioinformatics

Sequence assembly by the shotgun approach

Master sequence  short sequences, simply by examining the sequences for overlaps.

No need any prior knowledge of the genome.

What is Gene?

CGGTTGAAAGCGGTAGCGTCCATGCGTATTACTCTTGAGCGGTCGAACCTTCTGAAATCGCT

GAACCACGTCCACCGGGTCGTCGAGCGTCGCAACACGATCCCGATCCTGTCCAACGTTCTGC

……………………………………………………………………………………….ACTGAATTTTCTCA GTTACGACATTTTGCCTTGTTTTTGCGCCAAATGGGATCAACAGTACGTAACAATTTTTTGAC

AATGACCAATACATCCGAGGGGAATCATGGCACTCAACCTGAAGCAACGGCTT

1. Transcriptional control sites · 2. Non-coding RNAs · 3. mRNAs

Evidence-based prediction

Cis-alignment Trans-alignment Ab initio / de novo prediction Three Layers of Genome Annotation.

From Stein, L. 2001. Nature Reviews genetics 2:493-503

Some models

Dynamic programming Hidden Markov Models (HMMs) Conditional random field (CRF) Support vector machines (SVMs)