Free paper BioComputing from PSB

The Pacific Symposium on Biocomputing (PSB) is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Papers and presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2008 will be held January 4-8, 2008 at the Fairmont Orchid on the Big Island of Hawaii. Tutorials will be offered prior to the start of the conference.

PSB 2008 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. PSB is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.

The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders in the emerging areas and targeted to provide a forum for publication and discussion of research in biocomputing’s “hot topics.” In this way, PSB provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

Free papers from 1996 - 2006 here

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Introduction to Computational Proteomics

Jacques Colinge, Keiryn L. Bennett

Abbreviations: ESI, electrospray ionization; HMM, hidden Markov model; LC, liquid chromatography; MALDI, matrix assisted laser desorption ionization; MS, mass spectrometry; MS/MS, tandem mass spectrometry; PMF, peptide mass fingerprinting; PTM, posttranslational modifications; TOF, time-of-flight; SPC, shared peak count

Introduction

Proteomics is defined as the protein complement of the genome and involves the complete analysis of all the proteins in a given sample [1,2]. Several technologies are involved, and numerous questions concerning the proteins are addressed. What proteins are contained in a biological sample? At what concentration do the proteins exist? How do protein expression levels alter in different samples? What are the posttranslational modifications (PTMs)? Where in the cell [3] or an organism [4] are the proteins localised? How do the proteins interact with other proteins or molecules [5,6]?

The following discussion concentrates on computational aspects of protein identification. Characterization (identification of protein modifications), quantitation, and sample comparisons are also discussed briefly.

A typical proteomic experiment involves the analysis of complex samples, i.e., containing many proteins at varying concentrations [7]. Most of the currently available technology for identifying proteins from biological samples simply cannot contend with the complexity, and the majority of the low-abundance proteins are not observed. There are, however, a number of methods to separate the proteins contained in the original sample to obtain a simpler sample set that is amenable to in-depth analyses. Typical technologies are electrophoretic gels [8] and liquid chromatography [9] (LC) (see Figure 1A).

Figure 1. Steps in Sample Analysis by Proteomics

(A) Sample complexity reduction via an LC column. This is applicable to both proteins and peptides. It is possible to collect fractions at fixed or variable time intervals to obtain a series of less complex samples; however, direct MS analysis is also an option. The figure illustrates how peptides/proteins 1–11 are fractionated.

(B) Major steps in “bottom-up” proteomics and combinations thereof. Optional steps and essential steps are in rounded and bold rectangles, respectively. Green represents shotgun peptide sequencing entire sample digestion followed by multidimensional LC separation of peptides. Blue represents the classical gel approach, with or without (dashed arrows) peptide LC. Red combines protein and peptide LC.

(C) Data-dependent MS/MS analysis. Here, ESI of a liquid sample and alternation of the instrument between MS and MS/MS modes is illustrated. The data generated is a sequence of peptide experimental m/z associated with the corresponding fragments m/z. The complete analysis is named an LC-MS run.

A dominant and well-practiced technique in proteomics is referred to as the “bottom-up” approach. Proteins are digested into peptides (smaller components of the protein) by a proteolytic enzyme, e.g., trypsin. Analysis of the peptides is achieved by mass spectrometry (MS), and, from the data generated, the peptides (and subsequently the proteins) can be identified. The resultant mixture of peptides obtained from the digestion of several proteins is often highly complex, and a degree of separation can be achieved by peptide LC. Possible combinations of separation techniques are illustrated in Figure 1B.

Mass spectrometers comprise three main components: an ion-source, a fragmentation cell, and a mass analyzer. Each component is essentially independent from the others, and as such it is possible to combine the different technological aspects to produce different types of mass spectrometers. To measure its molecular mass, a molecule must be ionised. This occurs in the ion source of the mass spectrometer. The source can be based either on electrospray ionization [10] (ESI), which is therefore appropriate for liquid samples; or on matrix assisted laser desorption ionization [11] (MALDI), which is appropriate for samples that have been mixed with a matrix and crystallized on a metallic plate. The most common types of mass analyzers used in proteomic laboratories are (i) ion trap (IT), where the radio frequency of the trap is varied and the ejected ions are detected; and (ii) time-of-flight (TOF) analyzers, where the time required for an ion to “fly” through an electric field–free region of the instrument is recorded and correlated to the mass of the ion. Most current instruments include a fragmentation cell that uses an inert gas to break the peptides by collision-induced dissociation (CID). A fragmentation cell, however, is not always present (see next section), or fragmentation can occur “spontaneously” (in-source and post-source decay). All mass spectrometers do not measure mass directly, but rather the mass-to-charge ratio. Hence the measurements obtained are dependent on the charge state(s) of the molecule.

here the paper from PLOS

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Sains 2020: Computational Biology

Musim panas 2005, berkumpul pakar (international expert group) dalam suatu workshop (disponsori oleh Microsoft) untuk mendefinisikan dan menghasilkan suatu visi baru dan ‘roadmap’ dari evolusi, tantangan dan potensi penelitian ‘computer science’ dan ‘computing in science’ untuk 15 tahun ke depan.

Dokumen hasil workshop, Towards 2020 Science, memuat tantangan dan kesempatan yang muncul akibat peningkatan sintesis dari komputasi dan sains. Dokumen juga menam- pilkan hasil identifikasi akan hal-hal penting yang diperlukan untuk mempercepat kemajuan saintifik, terutama yang di-drive ‘computational sciences’ dan the ‘new kinds’ of science’ : sintesis komputasi dan sains. Sintesis ini memuncul bidang baru dan advances mulai dari genomics dan proteomics, ilmu bumi dan klimatology, material nano, kimia dan fisika.

Para pakar berharap, Towards 2020 Science akan menjadi suatu ‘pathfinder’ bagi arah penelitian baru dalam sain dan komputasi.

Hal penting yang perlu di catat adalah bahwa menuju tahun 2020: a) riset lebih di arahkan ke Computational Biology (dan tahun 2020 riset computational biology sudah benar-benar ‘establish), b) Ilmu komputer akan menjadi tool utama (core tools) wajib bagi ilmu non-eksakta, sebagaimana matematika menjadi tool bagi semua ilmu.

Sudah saat bagi kita untuk ikut serta melakukan penelitian pada computional biology. Beberapa link sebagai pengantar computational biology:

http://www.biodirectory.com/biowiki/Main_Page

http://www.biodirectory.com/biowiki/Computational_biology

http://en.wikipedia.org/wiki/Computational_biology

http://www.soe.ucsc.edu/~karplus/compbio_pages.html

http://www.cmu.edu/bio/education/courses/03510/LectureNotes/

http://compbiol.plosjournals.org/

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Semantic e-Science untuk Biomedicine

BMC bioinformatics telah menerbitkan sebuah supplement dengan issue khusus terkait dengan Semantic e-science for biomedicine. Supplement memuat enam papers yang up-to- date terkait penggunaan “semantic web technologies” pada life-sciences atau biomedicine. Topik-topik meliputi mulai modelling networks sampai ke “traditional Chinese Medicine”.

Salah satu paper yaitu Advancing translational medicine research with the semantic web menyajikan sebuah overview dari “W3C Semantic Web Health Care and Life Sciences Interest Group (HCLSIG)”. Paper ini mendeskripsikan tujuan dari HCLSIG dan sebuah contoh use-case yang mengcover pencapaian dan arah kedepan dari grup. Tidak seperti kebanyakan paper-paper “Semantic Web”, paper-paper menyajikan suatu gambaran dasar yang baik mengenai teknologi dan tantangan semantic web pada biomedicine, dan tidak memproklamirkan untuk menyelesaikan masalah ‘bio-data’ melalui penerapan sebuah set teknologi baru.

PASSaGE: Software for Searching Biological Image

Researchers at the Arizona State University (ASU) are working on software tools to analyze databases of biological images, that’s called PASSaGE (Pattern Analysis, Spatial Statistics and Geographic Exegesis). One of these projects is using machine learning technology to compare the expression patterns captured in the images. So far, the software was used to explore a database of embryonic fruit flies images to see if the genes share the same spatial patterns. This would indicate that these genes also share similar functions. The goal of the developers is to build a tool able to search biological image databases as fast as Internet search engines are doing.

If you want more information about this project, you can read a paper presented last year at the Computational Systems Bioinformatics Conference (CSB), “Classification of Drosophila embryonic developmental stage range based on gene expression pattern images” (PDF format, 6 pages, 2.52 MB).

Overview of PASSaGE

Spatial analysis is a fundamental part of scientific inquiry, including ecological, evolutionary and environmental science, epidemiology, geology, geography, and mathematics. Recent technological advances in genome sequencing, global positioning systems, and remote sensing have led to a rapid expansion of the number and size of spatially explicit datasets available for analysis. These new data have advanced the scope of spatial analysis to an even braoder variety of human endeavors, but have also rapidly outpaced the capabilities of traditional spatial analytic software and methods.

The need to overcome data limitations inherent in much of the specialized spatial analysis programs commonly available led to the development of PASSaGE: Pattern Analysis, Spatial Statistics, and Geographic Exegesis, a free, easy-to-use program for general spatial analysis. With a fairly simple point-and-click, mouse- and menu-driven interface, but flexible and powerful analysis customization, PASSaGE has been a very popular system for analyzing data in spatial context in both the laboratory and the classroom. The first version of PASSaGE has been downloaded by thousands of users from over 57 countries and 145 U.S. universities.

The Software PASSaGe is free (Windows, Linux, and Macintosh) and could be download here

A collage of fruit fly gene expression imagesAs an example, you can see on the left a collage of fruit fly gene expression images. “The proper development of each football-shaped fly embryo depends on the coordinated expression of thousands of genes. By studying the expression pattern of single genes, typically displayed in wide bands or narrow striped patterns, scientists can gain insight into the control and regulation of large genetic networks. Similar gene networks are found throughout biology, and break downs in these processes may result in birth defects, heart disease, cancer and aging. (Credit for image and caption: Biodesign News at ASU)

Free Journal on Computational Biology

Free journal on Computational Biology from Public Library of Science (PLoS) could found here in this link

PloS is a nonprofit organization of scientists and physicians committed to making the world’s scientific and medical literature a freely available public resource. Open Access: Everything PLoS publish is freely available online for you to read, download, copy, distribute, and use (with attribution) any way you wish.

There are other journal publisched by PLoS (biology, medicine, genetics, etc)

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