Research Areas in Bioinformatics

S B Mirza1314

Research Areas in Bioinformatics

ContentsIntroduction:................................................................................................................................................3

Bioinformatics research areas......................................................................................................................3

Sequence analysis........................................................................................................................................3

Genetic and population analysis..................................................................................................................4

Structural bioinformatics.............................................................................................................................4

Text mining and ontologies.........................................................................................................................4

Emerging areas............................................................................................................................................4

Bioinformatics education.............................................................................................................................4

Genetic and population analysis..................................................................................................................4

Computational evolutionary biology...................................................................................................5

Measuring biodiversity........................................................................................................................5

Gene expression analysis.....................................................................................................................5

Regulation analysis..............................................................................................................................6

Protein expression analysis..................................................................................................................6

Analysis of mutations in cancer...........................................................................................................6

Structure prediction.............................................................................................................................7

Modeling biological systems...............................................................................................................7

High-throughput image analysis..........................................................................................................7

Software tools..........................................................................................................................................8

S B Mirza1314 Page 1


Introduction:Bioinformatics or Computational Biology is the use of techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution.

Bioinformatics research areas:

Among the numerous areas of bioinformatics endeavour, traditional avenues such as sequence analysis,genetic andpopulation analysis, structural bioinformatics, text min-ing and ontologies are represented in this supplement,while chemoinformatics and biodiversity informaticsembody emerging bioinformatics themes. In order tocarry out bioinformatics research, innovative teaching is a prerequisite. Improvement in bioinformatics learning isevident from the case study using e-learning tools. Large-scale analysis of "-omics" data have been presented in the InCoB2009 BMC Genomics supplement, along withthe description of a minimum set of bioinformatics skills in a bioinformatics graduate.

Sequence analysis:

Choo et al. have benchmarked currently available N- terminal signal peptide prediction methods.Since the Phage Î¦-X174; was sequenced in 1977, the DNA sequences of more and more organisms have been decoded and stored in electronic databases. This data is analyzed to determine genes that code for proteins, as well as regulatory sequences. A comparison of genes within a species or between different species can show similarities between protein functions, or relations between species (the use of molecular systematics to construct phylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Today, computer programs are used to search the genome of thousands of organisms, containing billions of nucleotides. These programs can compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, in order to identify sequences that are related, but not identical. A variant of this sequence alignment is used in the sequencing process itself. The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research to sequence the first bacterial genome, Haemophilus influenza) does not give a sequential list of nucleotides, but instead the sequences of thousands of small DNA fragments (each about 600-800 nucleotides long). The ends of these fragments overlap and, when aligned in the right way, make up the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. In the case of the Human Genome Project, it took several months of CPU time (on a circa-2000 vintage DEC Alpha computer) to assemble the fragments. Shotgun sequencing is the method of choice for virtually all genomes sequenced today, and genome assembly algorithms are a critical area of bioinformatics research.



Another aspect of bioinformatics in sequence analysis is the automatic search for genes and regulatory sequences within a genome. Not all of the nucleotides within a genome are genes. Within the genome of higher organisms, large parts of the DNA do not serve any obvious purpose. This so-called junk DNA may, however, contain unrecognized functional elements. Bioinformatics helps to bridge the gap between genome and proteome projects, for example in the use of DNA sequence for protein identification.

Genetic and population analysis:

Kim have developed a genome browser to analyse the variation between the first sequenced Korean genome and other human genomes, to understand predisposition to disease and contribute to preventive health. From entire genomes, Veronika have used a novel bioimaging analysis method to identify populations of cells in a single culture.

Structural bioinformatics:Lee and describe a new approach to functionally annotate protein sequences by identifying homologous domains, while Dastidar have studied the role of the dynamics of Y100 in the recognition of the tumour suppressor protein p53.

Text mining and ontologies:Hsu propose a new machine learning approach to identifying abbreviations and definitions in biomedical texts. Applications of text mining include protein subcellular localization prediction and extracting key genes related to specific medical condition .

Emerging areas:

Bioinformatics methods are increasingly applied to areas beyond traditional biological data. A chemoinformatic analysis of present day drugs is presented by Khanna and Ranganathan while a biodiversity database on Korean birds is provided by Paik .

Bioinformatics education:Lim share their success of implementing e-learning tools to enhance undergraduate bioinformatics teaching and learning.

Genetic and population analysis:

Kim have developed a genome browser to analyse the variation between the first sequenced Korean To foster bioinformatics, it is important to actively mentor the next generation of bioinformaticians. The tips provided are by no means comprehensive and will also need to be updated constantly to tackle emerging challenges in mining biological data, be it for genome annotation, personal medicine, drug design or conserving our biodiversity.



Computational evolutionary biology

Evolutionary biology is the study of the origin and descent of species, as well as their change over time. Informatics has assisted evolutionary biologists in several key ways; it has enabled researchers to:

trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone,

more recently, compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, lateral gene transfer, and the prediction of bacterial speciation factors,

build complex computational models of populations to predict the outcome of the system over time

track and share information on an increasingly large number of species and organisms

Future work endeavours to reconstruct the now more complex tree of life.

The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology. Work in this area involves using specialized computer software to improve equations, algorithms, or integrated circuit designs. It is inspired by evolutionary principles such as replication, diversification through recombination or mutation, fitness, survival through selection or culling, and iteration, collectively called a Darwinian machine or Darwinian ratchet.

Measuring biodiversity

Biodiversity of an ecosystem might be defined as the total genomic complement of a particular environment, from all of the species present, whether it is a biofilm in an abandoned mine, a drop of sea water, a scoop of soil, or the entire biosphere of the planet Earth. Databases are used to collect the species names, descriptions, distributions, genetic information, status and size of populations, habitat needs, and how each organism interacts with other species. Specialized software programs are used to find, visualize, and analyze the information, and most importantly, communicate it to other people. Computer simulations model such things as population dynamics, or calculate the cumulative genetic health of a breeding pool (in agriculture) or endangered population (in conservation). One very exciting potential of this field is that entire DNA sequences, or genomes of endangered species can be preserved, allowing the results of Nature's genetic experiment to be remembered in silico, and possibly reused in the future, even if that species is eventually lost.

Gene expression analysis

The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research



area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression (HT) studies. HT studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the proteins that cancer up-regulates and down-regulates.

Regulation analysis

Regulation is the complex orchestra of events starting with an extra-cellular signal and ultimately leading to the increase or decrease in the activity of one or more protein molecules. Bioinformatics techniques have been applied to explore various steps in this process. For example, promoter analysis involves the elucidation and study of sequence motifs in the genomic region surround the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). One can then apply clustering algorithms to that expression data to determine which genes are co-expressed. Further analysis could take a variety of directions: one 2004 study analyzed the promoter sequences of co-expressed (clustered together) genes to find common regulatory elements and used machine learning techniques to identify the promoter elements involved in regulating each cluster[1].

Protein expression analysis

Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former involves a number of the same problems involve in examining microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete, peptides from each protein are detected.

Analysis of mutations in cancer

Massive sequencing efforts are currently underway to identify point mutations in a variety of genes in cancer. The sheer volume of data produced requires automated systems to read sequence data, and to compare the sequencing results to the known sequence of the human genome, including known germline polymorphisms.

Oligonucleotide microarrays, including comparative genomic hybridization and single nucleotide polymorphism arrays, able to probe simultaneously up to several hundred thousand sites throughout the genome are being used to identify chromosomal gains and losses in cancer. Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes from often noisy data. Further informatics approaches are being developed to understand the implications of lesions found to be recurrent across many tumors.



Structure prediction

Protein structure prediction is another important application of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. In the vast majority of cases, this primary structure uniquely determine a structure in its native environment. (Of course, there are exceptions, such as the bovine spongiform encephalopathy - aka Mad Cow Disease - prion.) Knowledge of this structure is vital in understanding the function of the protein. For lack of better terms, structural information are usually classified as one of secondary, tertiary and quaternary structures. A viable general solution to such predictions remains an open problem. As of now, most efforts have been directed towards heuristics that work most of the time.

One of the key ideas in bioinformatics research is the notion of homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In the structural branch of bioinformatics homology is used to determine which parts of the protein are important in structure formation and interaction with other proteins. In a technique called homology modelling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. This currently remains the only way to predict protein structures reliably.

One example of this is the similar protein homology between hemoglobin in humans and the hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in both organisms. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.

Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.

See also structural motif and structural domain.

Modeling biological systems

Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

High-throughput image analysis

Computational technologies are also used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content Biomedical imagery. Modern image analysis systems augment the observers ability to make measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. While these systems are not unique to



biology related imagery, their application to biologic problems continue to provide unique challenges and solutions, placing several imagery application under the umbrella of Bioinformatics. These systems are in the process of becoming more important for both diagnostics and research. Some examples:

high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology)

morphometrics are used to analyze pictures of embryos to track and to predict the fate of cell clusters during morphogenesis

clinical image analysis and visualization determine the real-time air-flow patterns in breathing lungs of living individuals before

and during challenge quantify occlusion size in real-time imagery from the development of and recovery

during arterial injury making behavioural observations from extended video recordings of laboratory animals infrared measurements for metabolic activity determination

Biomedical informatics, Biologically-inspired computing, List of publications in bioinformatics, Molecular modelling, Morphometrics, Metabolic network

Software tools

The computational biology tool best-known among biologists is probably BLAST, an algorithm for searching large sequence (protein, DNA) databases. NCBI provides a popular implementation that searches their massive sequence databases.

Computer scripting languages such as Perl and Python are often used to interface with biological databases and parse output from bioinformatics programs.

Bioinformatic meta search engines (Entrez, Bioinformatic Harvester) help finding relevant information from several databases.

Communities of bioinformatics programmers have set up free/open source projects such as EMBOSS, Bioconductor, BioPerl, BioLinux, BioPython, BioRuby, and BioJava which develop and distribute shared programming tools and objects (as program modules) that make bioinformatics easier.

There are also free WEB-based softwares designed for Structural Bioinformatics such as STING.

An integrated software workbench consisting of many free/open source tools described above and many others is known as VigyaanCD.

Taverna an open-source bioinformatics workbench that utilises a workflow model of experimental design. Taverna is included as part of the myGRID package of e-science software.



References:

1. Ranganathan S, Eisenhaber F, Tong JC, Tan TW: Extending Asia Pacific bioinformatics into new realms in the "-omics" era. BMC Genomics 2009, 10(Suppl 3):S1.

2. Choo KH, Tan TW, Ranganathan S: A comprehensive assess- ment of N-terminal signal peptides prediction methods. BMC Bioinformatics 2009, 10(Suppl 15):S2.

3. Kim WY, Kim SY, Kim TH, Ahn SM, Byun HN, Kim D, Kim DS, Lee YS, Ghang H, Park D, Kim BC, Kim C, Lee S, Kim SJ, Bhak J: Gevab: a prototype Genome Variation Analysis Browsing Server. BMC Bioinformatics 2009, 10(Suppl 15):S3.

4. Veronika M, Evans J, Matsudaira P, Welsch R, Rajapakse JS: Subpopulation analysis based on temporal features of high content images. BMC Bioinformatics 2009, 10(Suppl 15):S4.

5. Lee B, Lee D: Protein comparison at the domain architecture level. BMC Bioinformatics 2009, 10(Suppl 15):S5.

6. Dastidar SG, Lane DP, Verma CS: Modulation of p53 binding to MDM2: computational studies reveal important roles of Tyr100. BMC Bioinformatics 2009, 10(Suppl 15):S6.

7. Kuo CJ, Ling MHT, Lin KT, Hsu CN: BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature. BMC Bioinformatics 2009, 10(Suppl 15):S7.

8. Lin HN, Chen CT, Sung TY, Ho SY, Hsu WL: Protein subcellular localization prediction of eukaryotes using a knowledge- based approach. BMC Bioinformatics 2009, 10(Suppl 15):S8.

9. Tsai RTH, Lai PT, Dai HJ, Huang CH, Bow YY, Chang YC, Pan WH, Hsu WL: HypertenGene: Extracting key hypertension genes from biomedical literature with position and automatically- generated template features. BMC Bioinformatics 2009, 10(Suppl 15):S9.

10. Khanna V, Ranganathan S: Physiochemical property space distri- bution among human metabolites, drugs and toxins. BMC Bioinformatics 2009, 10(Suppl 15):S10.

11. Paik IH, Lim J, Chun BS, Jin SD, Yu JP, Lee JW, Bhak J, Paek WK: The Korean Bird Information System (KBIS) through open and public participation. BMC Bioinformatics 2009, 10(Suppl 15):S11.


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