Literature DB >> 16471013

Bioinformatics challenges in proteomics.

Claudia C Englbrecht1, Axel Facius.   

Abstract

A little after the genomic revolution had been celebrated, it seemed as if a competition began to found new -omics disciplines that ultimately all have the same goal, the understanding of biological function. There are many similar definitions for proteomics that can be summarized as follows: proteomics is a large-scale study of structure and function of proteins in an organism or cell. Importantly, the proteome is much more variable than the genome through its interactions with the genome and secondary modifications. It differs depending on the tissue and stage in life-cycle. Hence, proteomics is a very diverse discipline that uses a variety of experimental set-ups and targets in order to elucidate function. Its dissociation from other disciplines can only remain artificial. The bioinformatics applied to proteomics are equally varied. In this review we will focus mainly on a few areas of bioinformatics that seem to us as particularly noteworthy or characteristic for proteomics research, for example in 2DE analysis or mass spectrometry. Another important task of bioinformatics is the prediction of functional properties. We will summarize the approaches taken in order to predict protein networks, which are based on the extensive integration of several kinds of -omics data. We will give a short overview of a demanding field in computational biology, the analysis and prediction of protein 3D structures. In order to provide a broader perspective we will close this review with a generalized description of activities and databases in the realm of proteomics.

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Year:  2005        PMID: 16471013     DOI: 10.2174/138620705774962454

Source DB:  PubMed          Journal:  Comb Chem High Throughput Screen        ISSN: 1386-2073            Impact factor:   1.339


  2 in total

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Authors:  Hai-Jun Zhou; Yin-Kun Liu; Zhuozhe Li; Dong Yun; Qiang-Ling Shun; Kun Guo
Journal:  J Cancer Res Clin Oncol       Date:  2007-04-26       Impact factor: 4.553

2.  RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control Tool.

Authors:  David Morgenstern; Rotem Barzilay; Yishai Levin
Journal:  J Proteome Res       Date:  2021-03-03       Impact factor: 4.466

  2 in total

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