Assembling proteomics data as a prerequisite for the analysis of large scale experiments

<p>Abstract</p> <p>Background</p> <p>Despite the complete determination of the genome sequence of a huge number of bacteria, their proteomes remain relatively poorly defined. Beside new methods to increase the number of identified proteins new database applications are necessary to store and present results of large- scale proteomics experiments.</p> <p>Results</p> <p>In the present study, a database concept has been developed to address these issues and to offer complete information via a web interface. In our concept, the Oracle based data repository system SQL-LIMS plays the central role in the proteomics workflow and was applied to the proteomes of <it>Mycobacterium tuberculosis</it>, <it>Helicobacter pylori</it>, <it>Salmonella typhimurium </it>and protein complexes such as 20S proteasome. Technical operations of our proteomics labs were used as the standard for SQL-LIMS template creation. By means of a Java based data parser, post-processed data of different approaches, such as LC/ESI-MS, MALDI-MS and 2-D gel electrophoresis (2-DE), were stored in SQL-LIMS. A minimum set of the proteomics data were transferred in our public 2D-PAGE database using a Java based interface (Data Transfer Tool) with the requirements of the PEDRo standardization. Furthermore, the stored proteomics data were extractable out of SQL-LIMS via XML.</p> <p>Conclusion</p> <p>The Oracle based data repository system SQL-LIMS played the central role in the proteomics workflow concept. Technical operations of our proteomics labs were used as standards for SQL-LIMS templates. Using a Java based parser, post-processed data of different approaches such as LC/ESI-MS, MALDI-MS and 1-DE and 2-DE were stored in SQL-LIMS. Thus, unique data formats of different instruments were unified and stored in SQL-LIMS tables. Moreover, a unique submission identifier allowed fast access to all experimental data. This was the main advantage compared to multi software solutions, especially if personnel fluctuations are high. Moreover, large scale and high-throughput experiments must be managed in a comprehensive repository system such as SQL-LIMS, to query results in a systematic manner. On the other hand, these database systems are expensive and require at least one full time administrator and specialized lab manager. Moreover, the high technical dynamics in proteomics may cause problems to adjust new data formats. To summarize, SQL-LIMS met the requirements of proteomics data handling especially in skilled processes such as gel-electrophoresis or mass spectrometry and fulfilled the PSI standardization criteria. The data transfer into a public domain via DTT facilitated validation of proteomics data. Additionally, evaluation of mass spectra by post-processing using MS-Screener improved the reliability of mass analysis and prevented storage of data junk.</p

Efficacy of Osteoporosis Diagnosis Using DXA Scans of the Distal Radius in a Group of Male Patients with Osteoporosis: a Retrospective Study

Osteoporosis is a disease characterized by low bone mineral density (BMD), which compromises bone tissue increasing fragility and susceptibility to fracture. It affects nearly 50% of women and 20% of men over the age of 50, and fractures resulting from osteoporosis cause significant morbidity and mortality. Therefore, patients with or at risk for osteoporosis should be identified before rather than after a fracture occurs. The gold standard in diagnosing patients with osteoporosis is dual X-ray absorptiomerty (DXA). The purpose of this study is to evaluate the usefulness of assessing BMD at various parts of the distal radius (ultra-distal, mid-point, one third, and total) compared to the conventional sites (lumbar vertebrae and proximal femur) using DXA to diagnose osteoporosis. This was a retrospective study on 1,641 male patients over the age of 50 who had undergone bone densitometry (DXA scans) of at least one hip, lumbar vertebrae and distal radius. Ordinary regression and correlation analysis was used to assess the association between the lowest of the bone density scores of the hip or lumbar vertebrae and scans at the various sites on the radius. Comparing standardized scores from the radius method with the lowest standardized scores from the hip or lumbar vertebrae, a highly significant correlation was found, R = 0.59, p \u3c 0.001 for the left UD radius, R =0.59, p \u3c 0.001 for left MD radius, R =0.54, p \u3c 0.001 for the left 1/3 radius, and R =0.60, p \u3c 0.001 for the total left radius. The results indicate that the left radius total is the most accurate in diagnosing osteoporosis in our study population. The results of this study can have far-reaching psychosocio-economic implications showing that DXA scans of the distal radius can be used to effectively diagnose osteoporosis by using inexpensive, low-technology, portable scanners. These findings are particularly relevant to the needs of the undeserved rural populations of Central Appalachia

Laboratory data and sample management for proteomics.

Proteomic experiments can be difficult to handle because of the large amount of data in different formats that is generated. Samples need to be managed and generated, data needs to be integrated with samples and annotation information. A laboratory information management system (LIMS) can be used to overcome some of the data handling problems. In this chapter, we discuss the role of a LIMS in the proteomics laboratory, and show two step-by-step examples of usage of the Proteios Software Environment (ProSE) to handle two different proteomics workflows