Chapter 1
Omics Technologies, Data and Bioinformatics Principles
Maria V. Schneider and Sandra Orchard
Abstract
We provide an overview on the state of the art for the Omics technologies, the types of omics data and the bioinformatics resources relevant and related to Omics. We also illustrate the bioinformatics chal-lenges of dealing with high-throughput data. This overview touches several fundamental aspects of Omics and bioinformatics: data standardisation, data sharing, storing Omics data appropriately and exploring Omics data in bioinformatics. Though the principles and concepts presented are true for the various dif-ferent technological .elds, we concentrate in three main Omics .elds namely: genomics, transcriptomics and proteomics. Finally we address the integration of Omics data, and provide several useful links for bioinformatics and Omics.
Key words: Omics, Bioinformatics, High-throughput, Genomics, Transcriptomics, Proteomics, Interactomics, Data integration, Omics databases, Omics tools
1. Introduction

The last decade has seen an explosion in the amount of biological data generated by an ever-increasing number of techniques enabling the simultaneous detection of a large number of altera-tions in molecular components (1). The Omics technologies uti-lise these high-throughput (HT) screening techniques to generate the large amounts of data required to enable a system level under-standing of correlations and dependencies between molecular components.
Omics techniques are required to be high throughput because they need to analyse very large numbers of genes, gene expression, or proteins either in a single procedure or a combina-tion of procedures. Computational analysis, i.e., the discipline now known as bioinformatics, is a key requirement for the study of the vast amounts of data generated. Omics requires the use of
Bernd Mayer (ed.), Bioinformatics for Omics Data: Methods and Protocols, Methods in Molecular Biology, vol. 719, DOI 10.1007/978-1-61779-027-0_1, . Springer Science+Business Media, LLC 2011
3

Schneider and Orchard
techniques that can handle extremely complex biological samples in large quantities (e.g. high throughput) with high sensitivity and speci.city. Next generation analytical tools require improved robustness, .exibility and cost ef.ciency. All of these aspects are being continuously improved, potentially enabling institutes such as the Wellcome Trust Sanger Sequencing Centre (see Note 1) to generate thousands of millions of base pairs per day, rather than the current output of 100 million per day (http://www. yourgenome.org/sc/nt).
However, all this data production makes sense only if one is equipped with the necessary analytical resources and tools to understand it. The evolution of the laboratory techniques has therefore to occur in parallel with a corresponding improvement in analytical methodology and tools to handle the data. The phrase Omics ? a suf.x signifying the measurement of the entire comple-ment of a given level of biological molecules and information ? encompasses a variety of new technologies that can help explain both normal and abnormal cell pathways, networks, and processes via the simultaneous monitoring of thousands of molecular com-ponents. Bioinformaticians use computers and statistics to perform extensive Omics-related research by searching biological databases and comparing gene sequences and proteins on a vast scale to identify sequences or proteins that differ between diseased and healthy tissues, or more general between different phenotypes.
“Omics” spans an increasingly wide range of .elds, which now range from genomics (the quantitative study of protein coding genes, regulatory elements and noncoding sequences), transcrip-tomics (RNA and gene expression), proteomics (e.g. focusing on protein abundance), and metabolomics (metabolites and meta-bolic networks) to advances in the era of post-genomic biology and medicine: pharmacogenomics (the quantitative study of how genetics affects a host response to drugs), physiomics (physiologi-cal dynamics and functions of whole organisms) and in other .elds: nutrigenomics (a rapidly growing discipline that focuses on iden-tifying the genetic factors that in.uence the body’s response to diet and studies how the bioactive constituents of food affect gene expression), phylogenomics (analysis involving genome data and evolutionary reconstructions, especially phylogenetics) and inter-actomics (molecular interaction networks). Though in the remain-der of this chapter we concentrate on an isolated few examples of Omics technologies, much of what is said, for example about data standardisation, data sharing, storage and analysis requirements are true for all of these different technological .elds.
There are already large amounts of data generated by these technologies and this trend is increasing, for example second and third generation sequencing technologies are leading to an exponential increase in the amount of sequencing data available. From a computational point of view, in order to address the

2. Materials
2.1. Genomics High-Throughput Technologies
2.2. Transcriptomics High-Throughput Technologies
Omics Technologies, Data and Bioinformatics Principles
complexity of these data, understand molecular regulation and gain the most from such comprehensive set of information, knowledge discovery ? the process of automatically searching large volumes of data for patterns ? is a crucial step. This process of bioinformatics analysis includes: (1) data processing and molecule (e.g. protein) identi.cation, (2) statistical data analysis,
(3) pathway analysis, and (4) data modelling in a system wide context. In this chapter we will present some of these analytical methods and discuss ways in which data can be made accessible to both the specialised bioinformatician, but in particular to the research scientist.
There are a variety of de.nitions of the term HT; however we can loosely apply this term to cases where automation is used to increase the throughput of an experimental procedure. HT tech-nologies exploit robotics, optics, chemistry, biology and image analysis research. The explosion in data production in the public domain is a consequence of falling equipment prices, the opening of major national screening centres and new HT core facilities at universities and other academic institutes. The role of bioinfor-matics in HT technologies is of essential importance.
High-Throughput Sequencing (HTS) technologies are used not only for traditional applications in genomics and metagenomics (see Note 2), but also for novel applications in the .elds of tran-scriptomics, metatranscriptomics (see Note 3), epigenomics (see Note 4), and studies of genome variation (see Note 5). Next gen-eration sequencing platforms allow the determination of the sequence data from ampli.ed single DNA fragments and have been developed speci.cally to lend themselves to robotics and par-allelisation. Current methods can directly sequence only relatively short (300?1,000 nucleotides long) DNA fragments in a single reaction. Short-read sequencing technologies dramatically reduce the sequencing cost. There were initial fears that the increase in quantity might result in a decrease in quality, and improvements in accuracy and read length are being looked for. However, despite this, these advances have signi.cantly reduced the cost of several sequencing applications, such as resequencing individual genomes
(2) readout assays (e.g. ChIP-seq (3) and RNAseq (4)).
The transcriptome is the set of all messenger RNA (mRNA) molecules, or “transcripts”, produced in one or a population of cells. Several methods have been developed in order to gain expression information at high throughput level.