The Python Papers Anthology
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seismic-py: Reading seismic data with Python
Get PDFThe field of seismic exploration of the Earth has changeddramatically over the last half a century. The Society of ExplorationGeophysicists (SEG) has worked to create standards to store the vastamounts of seismic data in a way that will be portable across computerarchitectures. However, it has been impossible to predict the needs of theimmense range of seismic data acquisition systems. As a result, vendors havehad to bend the rules to accommodate the needs of new instruments andexperiment types. For low level access to seismic data, there is need for astandard open source library to allow access to a wide range of vendor datafiles that can handle all of the variations. A new seismic software package,seismic-py, provides an infrastructure for creating and managing drivers foreach particular format. Drivers can be derived from one of the known formatsand altered to handle any slight variations. Alternatively drivers can bedeveloped from scratch for formats that are very different from any previouslydefined format. Python has been the key to making driver development easyand efficient to implement. The goal of seismic-py is to be the base systemthat will power a wide range of experimentation with seismic data and at thesame time provide clear documentation for the historical record of seismicdata formats
Filtering Microarray Correlations by Statistical Literature Analysis Yields Potential Hypotheses for Lactation Research
Get PDFBackgroundRecent studies have demonstrated that the cyclical nature of mouse lactation can be mirrored at the transcriptome level of the mammary glands but making sense of microarray results requires analysis of large amounts of biological information which is increasingly difficult to access as the amount of literature increases. Extraction of protein-protein interaction from text by statistical and natural language processing has shown to be useful in managing the literature. Correlations between gene expression across a series of samples is a simple method to analyze microarray data as it was found that genes that are related in functions exhibit similar expression profiles. Microarrays had been used to examine the transcriptome of mouse lactation and found that the cyclic nature of the lactation cycle as observed histologically is reflected at the transcription level. However, there has been no study to date using text mining to sieve microarray analysis to generate new hypotheses for further research in the field of lactational biology. ResultsOur results demonstrated that a previously reported protein name co-occurrence method (5-mention PubGene) which was not based on a hypothesis testing framework, it is generally statistically more significant than the 99th percentile of Poisson distribution-based method of calculating co-occurrence. It agrees with previous methods using natural language processing to extract protein-protein interaction from text as more than 96% of the interactions found by natural language processing methods to overlap with the results from 5-mention PubGene method. However, less than 2% of the gene co-expressions analyzed by microarray were found from direct co-occurrence or interaction information extraction from the literature. At the same time, combining microarray and literature analyses, we derive a novel set of 7 potential functional protein-protein interactions that had not been previously described in the literature.ConclusionsWe conclude that the 5-mention PubGene method is more stringent than the 99th percentile of Poisson distribution method for extracting protein-protein interactions by co-occurrence of entity names and literature analysis may be a potential filter for microarray analysis to isolate potentially novel hypotheses for further research
