2.7+Bioinformatics

=Bioinformatics=

//Before you begin//: Lessons are based on community-based, continually updated online sources such as [|Wikipedia]. Relevant terms for this lesson are listed under Topics and presented in a narrative format in the Read about sections. Click on each of the linked items and visit the Wikipedia article to get the most out of the lesson, and then hit the Back button on your browser to return to the lesson.

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=Goals=
 * Basic**
 * To understand the differences between computational biology, bioinformatics, and systems biology
 * To appreciate the application of bioinformatics to scientific and clinical domains of interest
 * Advanced**
 * Locating and applying bioinformatics tools on the web
 * Understanding the strengths and weaknesses of different bioinformatics tools
 * Learning simple scripting languages to automate bioinformatic analyses

=Topics= [|computational biology],[|bioinformatics], [|molecular biology], [|DNA microarray], [|tissue microarray], [|genomics], [|proteomics], [|systems biology], [|Translational Research Informatics], [|PERL], [|nanotechnology]

=Read about=

[|Bioinformatics] is the application of [|information technology] to a problem in the biological domain, most commonly [|molecular biology]. The applied [|information technology] is usually a result of [|computational biology] research which is focused on methodology development, such as [|algorithms] and [|computer simulation techniques]. Due to its data intensive nature, [|computational biology] relies heavily on [|machine learning] approaches. [|Systems biology] is a multi-disciplinary field, including [|bioinformatics] and [|computational biology], with the premise "the whole is greater than the sum of the parts". Due to the complexity of the systems studied, [|computer simulations] and [|numerical methods] are commonly used in [|systems biology] research.

[|Bioinformatics] applications usually involve the development of a computational [|software pipeline], basically using a series of programs in order to complete a certain analysis. Due to the use of multiple programs which are commonly from various sources, there are usually many different input and output formats. In order to create a unified pipeline, many [|file format] conversions may be required. The [|practical extraction and report language (PERL)] [|programming language] is adept at performing this functionality due to its ability to process strings effectively and efficiently using [|regular expressions]. It is an [|interpreted language] and thus used as "glue" between the more computationally intensive programs, normally written in a [|compiled language], like [|C] or [|FORTRAN]. A group of [|PERL modules], called [|BioPerl], incorporate commonly used functionality into a standardized set of [|application programming interfaces (APIs)]. There are many other languages that are also used in [|bioinformatics], including [|Java]/[|BioJava] and [|Python]/[|BioPython]. Many [|bioinformatics] [|pipelines] are implemented in the [|UNIX]/[|Linux] [|OS] environment and use [|shell scripting], [|awk] and [|sed] in addition to the aforementioned [|programming languages].

With the large amount of data from clinical and "[|-omics]" studies, [|bioinformatics] is poised to help fuel [|clinical translational research informatics] studies. The key point is the integration of clinical and molecular data in order to accelerate improved healthcare outcomes, commonly referred to as "bench to bedside". The hope is that this translational research will help bring along [|personalized medicine]. Potential translational research studies could include [|biomarker] validation studies, discovery of [|drug targets], as well as discovering novel disease [|genes] and molecular pathways. Many of the current studies have relied on associating [|single nucleotide polymorphisms (SNPs)] on the genomic scale to observable (commonly disease) states, known as [|genome-wide association studies (GWAS)]. There has also been significant research in trying to use [|gene expression] data, which is currently [|DNA microarrays], integrated with other data sources including clinical parameters, in order to tease apart the genes underlying disease states.

=Activities= Talk to your bioinformatics core or computational biology labs about collaborating on a translational research project.

=Online Resources=
 * Chandran, U. [|Introduction to bioinformatics] (powerpoint). APIII Conference: October 2008.
 * Butte, A. [|Translational Bioinformatics: Coming of Age]. JAMIA: Volume 15, Issue 6, November-December 2008, Pages 709-714.
 *  [|Selected proceedings of the 2009 Summit on Translational Bioinformatics]. San Francisco, CA, USA. 15 – 17 March 2009.

=Questions= (we can keep this blank for now)

=Advanced courses=
 * Isaac Kohane, Marco Ramoni. Course materials for HST.950J / 6.872J [|Engineering Biomedical Information: From Bioinformatics to Biosurveillance, Fall 2005] . MIT OpenCourseWare, Massachusetts Institute of Technology.
 * Leonid Mirny, Robert Berwick, Alvin Kho, Isaac Kohane. Course materials for HST.508 [|Quantitative Genomics, Fall 2005] . MIT OpenCourseWare, Massachusetts Institute of Technology.
 * George Church. Course materials for HST.508 [|Genomics and Computational Biology, Fall 2002] . MIT OpenCourseWare, Massachusetts Institute of Technology.
 * Gil Alterovitz, Manoli Kellis, Marco Ramoni. Course materials for 6.092 [|Bioinformatics and Proteomics, January (IAP) 2005] . MIT OpenCourseWare, Massachusetts Institute of Technology.

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