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<h3>1) Sequencing</h3>

Historically, sequencing was done in sequencing centers, centralized facilities (ranging from large independent institutions such as Joint Genome Institute which sequence dozens of terabases a year, to local molecular biology core facilities) which contain research laboratories with the costly instrumentation and technical support necessary. As sequencing technology continues to improve, however, a new generation of effective fast turnaround benchtop sequencers has come within reach of the average academic laboratory.~~~~ ~~~~On the whole, genome sequencing approaches fall into two broad categories, shotgun and high-throughput (aka next-generation) sequencing.

2) Assembly

3) Annotation

The DNA sequence assembly alone is of little value without additional analysis.~~~~ ~~~~Genome annotation is the process of attaching biological information to sequences, and consists of three main steps. ① Identifying portions of the genome that do not code for proteins, ② Identifying elements on the genome, a process called gene prediction, and ③ Attaching biological information to these elements.

Automatic annotation tools try to perform these steps in silico, as opposed to manual annotation which involves human expertise and potential experimental verification. Ideally, these approaches co-exist and complement each other in the same annotation pipeline. Traditionally, the basic level of annotation is using BLAST for finding similarities, and then annotating genomes based on homologues.~~~~ ~~~~More recently, additional information is added to the annotation platform. The additional information allows manual annotators to deconvolute discrepancies between genes that are given the same annotation. Some databases use genome context information, similarity scores, experimental data, and integrations of other resources to provide genome annotations through their Subsystems approach. Other databases rely on both curated data sources as well as a range of software tools in their automated genome annotation pipeline. Structural annotation consists of the identification of genomic elements, primarily ORFs and their localisation, or gene structure. Functional annotation consists of attaching biological information to genomic elements.

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Transcriptomics is the study of the transcriptome - the all set of RNA transcripts which are produced under specific circumstances in one cell or population of cells - using high throughout methods such as microarray analysis.

## Analysis ##

A number of organism-specific transcriptome databases have been constructed and annotated to aid in the identification of genes that are differentially expressed in distinct cell populations. RNA-Seq is emerging as the method of choice for measuring transcriptomes of organisms, though the older technique of DNA microarrays is still used.

1) RNA-Seq

RNA-seq (RNA sequencing), also called whole transcriptome shotgun sequencing, uses next-generation sequencing to reveal the presence and quantity of RNA in a biological sample at a given moment in time. RNA-Seq is used to analyze the continually changing cellular transcriptome. Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations, SNPs and changes in gene expression. In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA and ribosomal profiling. RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5’ and 3’ gene boundaries. Prior to RNA-Seq, gene expression studies were done with hybridization-based microarrays. Issues with microarrays include cross-hybridization artifacts, poor quantification of lowly and highly expressed genes, and the knowledge of the sequence. Because of these technical issues, transcriptomics transitioned to sequencing-based methods. These progressed from Sanger sequencing of Expressed Sequence Tag libraries, to chemical tag-based methods and finally to the current technology, NGS of cDNA.

2) DNA microarray

A DNA microarray is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles of a specific DNA sequence, known as proves. These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA (also called anti-sense RNA) sample under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target.

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Proteomics is the large-scale study of proteins, particularly their structures and fucntions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. The term proteomics was first coined in 1997 to make an analogy with genomics, the study of the genome. The word proteome is a portmanteau of protein and genome. ## Analysis ## 1) Protein detection with antibodies (immunoassays) Antibodies to particular proteins or to their modified forms have been used in biochemistry and cell biology studies. These are among the most common tools used by molecular biologists today. There are several specific techniques and protocols that use antibodies for protein detection. The enzyme-linked immunosorbent assay (ELISA) has been used for decades to detect and quantitatively measure proteins in samples. The Western blot can be used for detection and quantification of individual proteins, where in an initial step a complex protein mixture is separated using SDS-PAGE and then the protein of interested identified using an antibody. Modified proteins can be studied by developing an antibody specific to that modification. For example, there are antibodies that only recognize certain proteins when they are tyrosine-phosphorylated, known as phospho-specific antibodies. Also, there are antibodies specific to other modifications. These can be used to determine the set of proteins that have undergone the modification of interest. 2) Antibody-free protein detection While protein detection with antibodies are still very common in molecular biology, also other methods have been developed that do not rely on an antibody. These methods offer various advantages, for instance they are often able to determine the sequence of a protein or peptide, they may have higher throughput than antibody-based and they sometimes can identify and quantify proteins for which no antibody exists. Detection methods One of the earliest method for protein analysis has been Edman degradation where a single peptide is subjected to multiple steps of chemical degradation to resolve its sequence. These methods have mostly been supplanted by technologies that offer higher throughput. More recent methods use mass spectrometry-based techniques, a development that was made possible by the discovery of "soft ionization" methods such as matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI) developed in the 1980s. These methods gave rise to the top-down and the botton-up proteomics workflows where often additional separation is performed before analysis. <h4>Seperation methods</h4> For the analysis of complex biological samples, a reduction of sample complexity is required. This can be performed off-line by one-dimensional or two-dimensional separation. More recently, on-line methods have been developed where individual peptides (in bottom-up proteomics approaches) are separated using Reversed-phase chromatography and then directly ionized using ESI; the direct coupling of separation and analysis explains the term on-line analysis. <h3>3) High-throughput proteomic technologies</h3> Proteomics has steadily gained momentum over the past decade with the evolution of several approaches. Few of these are new and others build on traditional methods. Mass spectrometry-based methods and micro arrays are the most common technologies for large-scale study of proteins. <h4>Reverse-phased protein microarrays</h4> This is a promising and newer microarray application for the diagnosis, study and treatment of complex diseases such as cancer. The technology merges laser capture microdissection (LCM) with micro array technology, to produce reverse phase protein microarrays. In this type of microarrays, the whole collection of protein themselves are immobilized with the intent of capturing various stages of disease within an individual patient. When used with LCM, reverse phase arrays can monitor the fluctuating state of proteome among different cell population within a small area of human tissue. This is useful for profiling the status of cellular signaling molecules, among a cross section of tissue that includes both normal and cancerous cells. This approach is useful in monitoring the status of key factors in normal prostate epithelium and invasive prostate cancer tissues. LCM then dissects these tissue and protein lysates were arrayed onto nitrocellulose slides, which were probed with specific antibodies. This method can track all kinds of molecular events and can compare diseased and healthy tissues within the same patient enabling the development of treatment strategies and diagnosis. The ability to acquire proteomics snapshots of neighboring cell populations, using reverse phase microarrays in conjunction with LCM has a number of applications beyond the study of tumors. The approach can provide insights into normal physiology and pathology of all the tissues and is invaluable for characterizing developmental processes and anomalies.

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imported>Byeongeun Lee

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