There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

http://dmrocke.ucdavis.edu/Class/BST226.2014.Winter/edgeR%20bioinformatics%202010.pdf

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Integrated analysis of multimodal single-cell data

Successful treatment of recurrent pyogenic cholangitis depends on the ability to delineate the pathology of the entire biliary system and to eradicate all stones, strictures, and destroyed liver segments. Recent advances in imaging techniques and stone fragmentation technology have been applied successfully in the management of this condition. Apart from direct cholangiography and ultrasonography, valuable information can be obtained from computed tomography which is particularly helpful in patients with recurrent disease after previous biliary surgery. Application of electrohydraulic lithotripsy in this disease solves the problem of difficult stone retrieval due to large size, impaction behind relative strictures and inside angulated segmental bile ducts. Together with construction of a hepaticocutaneous jejunostomy serving as a permanent access to the bile duct, it is anticipated that the incidence of recurrence and reoperation in the future can be reduced.

Recurrent pyogenic cholangitis: current management.

Chemical proteomics or activity based proteomics is a functional proteomics technology where molecular probes are used to target a selective group of functionally related proteins. Its emergence has enabled specific targeting of subproteomes, overcoming the limitations in dynamic range of traditional large-scale proteomics experiments. Using a chemical proteomics strategy, we attempt to differentially profile the nucleotide-binding proteome of active and resting platelets. We apply an affinity chromatography protocol using immobilized adenosine triphosphate, cyclic adenosine monophosphate, and cyclic guanosine monophosphate. The specificity of the immobilized nucleotides was demonstrated by competitive assays and by immunoblotting. LC coupled MS/MS was applied to identify the proteins recovered by our chemical proteomics strategy. When compared to a standard set of platelet lysate proteins, we confirmed that enrichment for nucleotide-binding proteins was indeed taking place. Finally, by employing label-free MS-based comparative quantification, we found a small number of platelet proteins that show statistically significant difference between the active and resting nucleotide-binding proteome.

Activity profiling of platelets by chemical proteomics.

Small cell carcinoma of the esophagus is a rare and aggressive malignant tumor. Telomerase activation is common in human cancers. There is a lack of data on telomerase activity in esophageal small cell cancers. The present report studied the role of telomerase activity in esophageal small cell carcinoma. The clinicopathologic data of five patients with small cell carcinoma of the esophagus who underwent primary surgical treatment between 1991 and 2000 were studied. Telomeric repeat amplification protocol assays were used to investigate telomerase activity in these tumors. The proliferative activity (MIB-1) and p53 expression of these tumors were also studied using immunohistochemistry and correlated with the telomerase activity. All five small cell carcinomas showed detectable telomerase activity in the primary tumor. Two out of the five morphologically normal esophageal mucosae adjacent to the primary tumor had detectable telomerase activity. There was no correlation between the p53 expression, tumor stage, survival of patients, and the presence of telomerase activity. High MIB-1 expression in esophageal small cell carcinomas was associated with high telomerase activity. Telomerase activation is common in small cell carcinoma of the esophagus. This fact may find application in anti-telomerase treatment for this aggressive tumor.

Telomerase activity in small cell esophageal carcinoma.

In the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour. Focusing primarily on somatic single nucleotide mutations in this review, we consider both coding and non-coding driver mutations, and discuss how such mutations might be identified from cancer sequencing datasets. We describe some of the tools and database that are available for the annotation of somatic variants and the identification of cancer driver genes. We also address the use of genome-wide variation in mutation load to establish background mutation rates from which to identify driver mutations under positive selection. Finally, we describe the ways in which mutational signatures can act as clues for the identification of cancer drivers, as these mutations may cause, or arise from, certain mutational processes. By defining the molecular changes responsible for driving cancer development, new cancer treatment strategies may be developed or novel preventative measures proposed.

Finding cancer driver mutations in the era of big data research.

Selected reaction monitoring (SRM) is a mass spectrometry-based approach commonly used to increase analytical sensitivity and selectively for specific compounds in complex metabolomic samples. While the goal of well-designed SRM methods is to monitor for unique precursor–product ion pairs, in practice this is not always possible due to the diversity of the metabome and the resolution limits of mass spectrometers that are capable of SRM. Isobaric or near-isobaric precursor ions with different chromatographic properties but identical product ions often arise in complex samples. Without analytical standards, such metabolites will go undetected by conventional data analysis methods. Furthermore, a single SRM method may include simultaneous monitoring of tens to hundreds of different metabolites across multiple samples making quantification of all detected ions a challenging task. To facilitate the analysis of SRM data from complex metabolomic samples, we have developed the Metabolite Mass Spectrometry Analysi...

Jason W. H. Wong

Papers

Recurrent pyogenic cholangitis: current management.

Activity profiling of platelets by chemical proteomics.

Telomerase activity in small cell esophageal carcinoma.

Finding cancer driver mutations in the era of big data research.

MMSAT: automated quantification of metabolites in selected reaction monitoring experiments.