IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine最新文献

Next-generation sequencing (NGS) has brought human genomic research to an unprecedented era. RNA-Seq is a branch of NGS that can be used to quantify gene expression and depends on accurate annotation of the human genome (i.e., the definition of genes and all of their variants or isoforms). Multiple annotations of the human genome exist with varying complexity. However, it is not clear how the choice of genome annotation influences RNA-Seq gene expression quantification. We assess the effect of different genome annotations in terms of (1) mapping quality, (2) quantification variation, (3) quantification accuracy (i.e., by comparing to qRT-PCR data), and (4) the concordance of detecting differentially expressed genes. External validation with qRT-PCR suggests that more complex genome annotations result in higher quantification variation.

Next generation sequencing (NGS) technologies have been applied extensively in many areas of genetics and genomics research. A fundamental problem when comes to analyzing NGS data is mapping short sequencing reads back to the reference genome. Most of existing software packages rely on a single uniform reference genome and do not automatically take into the consideration of genetic variants. On the other hand, large proportions of incorrectly mapped reads affect the correct interpretation of the NGS experimental results. As an example, Degner et al. showed that detecting allele-specific expression from RNA sequencing data was biased toward the reference allele. In this study, we developed a method that utilize DirectX 11 enabled graphics processing unit (GPU)'s parallel computing power to produces a personalized diploid reference genome based on all known genetic variants of that particular individual. We show that using such a personalized diploid reference genome can improve mapping accuracy and significantly reduce the bias toward reference allele in allele-specific expression analysis. Our method can be applied to any individual that has genotype information obtained either from array-based genotyping or resequencing. Besides the reference genome, no additional changes to alignment algorithm are needed for performing read mapping therefore one can utilize any of the existing read mapping tools and achieve the improved read mapping result. C++ and GPU compute shader source code of the software program is available at: http://code.google.com/p/diploid-mapping/downloads/list.

We propose a web interface that allows researchers to quantify and analyze microtubule confocal images online. Most analyses of microtubule confocal images are performed manually using very simple software or tools. Analysis results are stored locally within each collaborator with different styles and formats. This has limited the sharing of data and results when collaborating among different research parties. A web interface provides a simple way for users to process data online. It also allows easy sharing of both data and results among different participating groups. Analysis workflow of the interface is made similar to existing manual protocols. We demonstrate the integration of image processing algorithm in the current workflow to aid the analysis. Our design also allows integration of novel automated analysis algorithms and modules to re-evaluate existing data. This interface can provide a validation platform for new automated algorithm and allow collaboration on microtubule image analysis from different locations.

Statistical inferences on RNA-Seq data, e.g., detecting differential gene expression, are meaningful only after proper normalization. However, there is no consensus for choosing a normalization procedure from among the many existing procedures. We evaluated several RNA-Seq normalization procedures by (1) correlating estimated RNA-Seq expression values to those of microarrays, (2) examining the concordance of stable and differential gene detection between the platforms, and (3) applying the procedures to simulated RNA-Seq data. Results suggested that RNA-Seq normalization procedures have little effect on both inter-platform gene expression correlation as well as inter-platform concordance of genes detected as stably or differentially expressed. However, the results of simulated analysis suggested that some normalization procedures are more robust to changes in distribution of differentially expressed genes. These results may provide guidance for selecting RNA-Seq normalization procedures.

Many complex information needs that arise in biomedical disciplines require exploring multiple documents in order to obtain information. While traditional information retrieval techniques that return a single ranked list of documents are quite common for such tasks, they may not always be adequate. The main issue is that ranked lists typically impose a significant burden on users to filter out irrelevant documents. Additionally, users must intuitively reformulate their search query when relevant documents have not been not highly ranked. Furthermore, even after interesting documents have been selected, very few mechanisms exist that enable document-to-document transitions. In this paper, we demonstrate the utility of assertions extracted from biomedical text (called semantic predications) to facilitate retrieving relevant documents for complex information needs. Our approach offers an alternative to query reformulation by establishing a framework for transitioning from one document to another. We evaluate this novel knowledge-driven approach using precision and recall metrics on the 2006 TREC Genomics Track.

Copy number variation (CNV) detection using SNP array data is challenging due to the low signal-to-noise ratio. In this study, we propose a principal component analysis (PCA) based correction to eliminate variance in CNV data induced by potential confounding factors. Simulations show a substantial improvement in CNV detection accuracy after correction. We also observe a significant improvement in data quality in real SNP array data after correction.

Doctors need fast and convenient access to medical data. This motivates the use of mobile devices for knowledge retrieval and sharing. We have developed TissueWiki^Mobile on the Apple iPhone and iPad to seamlessly access TissueWiki, an enormous repository of medical histology images. TissueWiki is a three terabyte database of antibody information and histology images from the Human Protein Atlas (HPA). Using TissueWiki^Mobile, users are capable of extracting knowledge from protein expression, adding annotations to highlight regions of interest on images, and sharing their professional insight. By providing an intuitive human computer interface, users can efficiently operate TissueWiki^Mobile to access important biomedical data without losing mobility. TissueWiki^Mobile furnishes the health community a ubiquitous way to collaborate and share their expert opinions not only on the performance of various antibodies stains but also on histology image annotation.

We developed a scalable distributed computing system using the Berkeley Open Interface for Network Computing (BOINC) to align next-generation sequencing (NGS) data quickly and accurately. NGS technology is emerging as a promising platform for gene expression analysis due to its high sensitivity compared to traditional genomic microarray technology. However, despite the benefits, NGS datasets can be prohibitively large, requiring significant computing resources to obtain sequence alignment results. Moreover, as the data and alignment algorithms become more prevalent, it will become necessary to examine the effect of the multitude of alignment parameters on various NGS systems. We validate the distributed software system by (1) computing simple timing results to show the speed-up gained by using multiple computers, (2) optimizing alignment parameters using simulated NGS data, and (3) computing NGS expression levels for a single biological sample using optimal parameters and comparing these expression levels to that of a microarray sample. Results indicate that the distributed alignment system achieves approximately a linear speed-up and correctly distributes sequence data to and gathers alignment results from multiple compute clients.

Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression maps on mouse brain. The experimental results show that the detected association rules make sense biologically. By inspecting the obtained clusters and the genes having the gene functions of frequent itemsets, interesting clues were discovered that provide valuable insight to biological scientists. Moreover, discovered association rules can be potentially used to predict gene functions based on similarity of gene expression maps.