A Thesis Is a Product Is a Tracking

In this work, I discuss how global products/ identities are made, transported and consumed, and the inevitable ‘mis-’ in acts of transmission. This research ranges from the miscommunication in languages and linguistics, to the gap between production and consumption. I investigate how things and humans are misread, mispronounced, misfit and mistranslated when they traverse social and cultural borders, arriving at a place in between languages, holding on to and letting go of things that are familiar to neither and both cultures. This work explores diverse media such as publications, videos and installations, and examines how they maybe used to address such contexts as factory production, global trade, circulation and tracking of commodities as well as identities

A Genome-Wide Study of Allele-Specific Expression in Colorectal Cancer

Accumulating evidence from small-scale studies has suggested that allele-specific expression (ASE) plays an important role in tumor initiation and progression. However, little is known about genome-wide ASE in tumors. In this study, we conducted a comprehensive analysis of ASE in individuals with colorectal cancer (CRC) on a genome-wide scale. We identified 5.4 thousand genome-wide ASEs of single nucleotide variations (SNVs) from tumor and normal tissues of 59 individuals with CRC. We observed an increased ASE level in tumor samples and the ASEs enriched as hotspots on the genome. Around 63% of the genes located there were previously reported to contain complex regulatory elements, e.g., human leukocyte antigen (HLA), or were implicated in tumor progression. Focussing on the allelic expression of somatic mutations, we found that 37.5% of them exhibited ASE, and genes harboring such somatic mutations, were enriched in important pathways implicated in cancers. In addition, by comparing the expected and observed ASE events in tumor samples, we identified 50 tumor specific ASEs which possibly contributed to the somatic events in the regulatory regions of the genes and significantly enriched known cancer driver genes. By analyzing CRC ASEs from several perspectives, we provided a systematic understanding of how ASE is implicated in both tumor and normal tissues and will be of critical value in guiding ASE studies in cancer

Classification of protein quaternary structure by functional domain composition

BACKGROUND: The number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences. RESULTS: To explore this problem, we adopted an approach based on the functional domain composition of proteins. Every protein was represented by a vector calculated from the domains in the PFAM database. The nearest neighbor algorithm (NNA) was used for classifying the quaternary structure of proteins from this information. The jackknife cross-validation test was performed on the non-redundant protein dataset in which the sequence identity was less than 25%. The overall success rate obtained is 75.17%. Additionally, to demonstrate the effectiveness of this method, we predicted the proteins in an independent dataset and achieved an overall success rate of 84.11% CONCLUSION: Compared with the amino acid composition method and Blast, the results indicate that the domain composition approach may be a more effective and promising high-throughput method in dealing with this complicated problem in bioinformatics

A Brief Review of Computational Gene Prediction Methods

With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions

Human transcriptional interactome of chromatin contribute to gene co-expression

BACKGROUND: Transcriptional interactome of chromatin is one of the important mechanisms in gene transcription regulation. By chromatin conformation capture and 3D FISH experiments, several chromatin interactions cases among sequence-distant genes or even inter-chromatin genes were reported. However, on genomics level, there is still little evidence to support these mechanisms. Recently based on Hi-C experiment, a genome-wide picture of chromatin interactions in human cells was presented. It provides a useful material for analysing whether the mechanism of transcriptional interactome is common. RESULTS: The main work here is to demonstrate whether the effects of transcriptional interactome on gene co-expression exist on genomic level. While controlling the effects of transcription factors control similarities (TCS), we tested the correlation between Hi-C interaction and the mutual ranks of gene co-expression rates (provided by COXPRESdb) of intra-chromatin gene pairs. We used 6,084 genes with both TF annotation and co-expression information, and matched them into 273,458 pairs with similar Hi-C interaction ranks in different cell types. The results illustrate that co-expression is strongly associated with chromatin interaction. Further analysis using GO annotation reveals potential correlation between gene function similarity, Hi-C interaction and their co-expression. CONCLUSIONS: According to the results in this research, the intra-chromatin interactome may have relation to gene function and associate with co-expression. This study provides evidence for illustrating the effect of transcriptional interactome on transcription regulation

Comparative Transcriptomes and EVO-DEVO Studies Depending on Next Generation Sequencing

High throughput technology has prompted the progressive omics studies, including genomics and transcriptomics. We have reviewed the improvement of comparative omic studies, which are attributed to the high throughput measurement of next generation sequencing technology. Comparative genomics have been successfully applied to evolution analysis while comparative transcriptomics are adopted in comparison of expression profile from two subjects by differential expression or differential coexpression, which enables their application in evolutionary developmental biology (EVO-DEVO) studies. EVO-DEVO studies focus on the evolutionary pressure affecting the morphogenesis of development and previous works have been conducted to illustrate the most conserved stages during embryonic development. Old measurements of these studies are based on the morphological similarity from macro view and new technology enables the micro detection of similarity in molecular mechanism. Evolutionary model of embryo development, which includes the “funnel-like” model and the “hourglass” model, has been evaluated by combination of these new comparative transcriptomic methods with prior comparative genomic information. Although the technology has promoted the EVO-DEVO studies into a new era, technological and material limitation still exist and further investigations require more subtle study design and procedure

A new strategy for better genome assembly from very short reads

<p>Abstract</p> <p>Background</p> <p>With the rapid development of the next generation sequencing (NGS) technology, large quantities of genome sequencing data have been generated. Because of repetitive regions of genomes and some other factors, assembly of very short reads is still a challenging issue.</p> <p>Results</p> <p>A novel strategy for improving genome assembly from very short reads is proposed. It can increase accuracies of assemblies by integrating <it>de novo </it>contigs, and produce comparative contigs by allowing multiple references without limiting to genomes of closely related strains. Comparative contigs are used to scaffold <it>de novo </it>contigs. Using simulated and real datasets, it is shown that our strategy can effectively improve qualities of assemblies of isolated microbial genomes and metagenomes.</p> <p>Conclusions</p> <p>With more and more reference genomes available, our strategy will be useful to improve qualities of genome assemblies from very short reads. Some scripts are provided to make our strategy applicable at <url>http://code.google.com/p/cd-hybrid/</url>.</p

Discrimination of approved drugs from experimental drugs by learning methods

<p>Abstract</p> <p>Background</p> <p>To assess whether a compound is druglike or not as early as possible is always critical in drug discovery process. There have been many efforts made to create sets of 'rules' or 'filters' which, it is hoped, will help chemists to identify 'drug-like' molecules from 'non-drug' molecules. However, among the chemical space of the druglike molecules, the minority will be approved drugs. Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods.</p> <p>Results</p> <p>Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules.</p> <p>Conclusion</p> <p>The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.</p