Techniques for integrating -omics data

Abstract

Abstract:The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present -omics community, because -omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.

Integrative biomarker discovery in neurodegenerative diseases

“…Some warehouses compile parts of the aforementioned databases, such as search tool for the retrieval of interacting genes/proteins (STRING) and iRefIndex . Bio Warehouse is another tool integrating several different databases and allowing data mining. See Azuaje for other examples of databases and tools for integrating and sharing biomedical data.…”

Foodomics strategies for the analysis of transgenic foods

“…Furthermore, data sets should be reproducible and acquired at high-throughput, then analysis of the system under study can be performed in a consistent, systematic manner [121]. Also, before data-integration procedures can be practically applied to GMO analysis, much effort will have to be made to develop effective integrative statistical approaches and appropriate computational frameworks for describing molecular systems and connecting omics databases [122,123].…”

Data Integration Protocol In Ten-steps (DIPIT): A new standard for medical researchers

“…Data file integration has enhanced knowledge across a broad spectrum of health and medical research, such as health employee research [4], behavioural survey data [5], social sciences [2], patient hospital records [4,[6][7][8], cancer and other health research [9,10] and in bio molecular systems [11][12][13][14], genetics and genomics [15,16]. In the field of health and medical science it is becoming an increasing requirement to integrate or merge extensive heterogeneous data files for research purposes, and data files can be linked from multiple providers to perform complex analyses [17][18][19].…”