Predicting molecular markers of development and regeneration through integrative analysis of high-throughput genomics data
In biology and medicine, the expression of particular gene transcripts and proteins is often used to identify particular cell types, developmental states, or pathogenic states to which they are unique. In this context, such transcripts and proteins are termed molecular markers. Molecular markers are indispensable in many facets of scientific research and can ultimately be used to reveal the biology behind their functions as such. However, in many cases, knowledge of which markers are ideal for a given task is still an elusive goal. In particular, developmental and stem cell research strongly relies on both protein and nucleotide molecular markers for numerous aspects as cell state and identity is very important in these fields. To identify protein-coding transcripts which may indicate novel stem cell marker proteins, a database of heterogeneous murine stem cell microarray data was analyzed using a novel clustering method. Analysis of transcripts enriched in markers demarcating undifferentiated and differentiated cells revealed that aggregate functions could be defined for markers of differentiated cells, but not for those in undifferentiated cells. In addition, the results indicated that genes expressed in mammalian stem cells and their immediate derivatives have common ancestors in the sea urchin, suggesting that mechanisms governing mammalian stem cells were established very early in evolution. To identify novel non-coding RNA markers in a muscle system, a ncRNA prediction approach enabling the detection of longer ncRNAs was used to identify miRNA containing sites in the mouse genome. The use of EST data was determined to improve ncRNA prediction strategies during the development of this method by evaluating the performance of miRNA prediction by combining information regarding structured RNA loci with EST data according to a miRNA biogenesis model. The methodology described herein was employed to identify and validate novel ncRNAs exhibiting potential control over Myf5 during myoblast differentiation.
Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: 4441.
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