Hyuk Cho

Evolutionary Analysis of the B56 Gene Family of PP2A Regulatory Subunits

Protein phosphatase 2A (PP2A) is an abundant serine/threonine phosphatase that functions as a tumor suppressor in numerous cell-cell signaling pathways, including Wnt, myc, and ras. The B56 subunit of PP2A regulates its activity, and is encoded by five genes in humans. B56 proteins share a central core domain, but have divergent amino- and carboxy-termini, which are thought to provide isoform specificity. We performed phylogenetic analyses to better understand the evolution of the B56 gene family. We found that B56 was present as a single gene in eukaryotes prior to the divergence of animals, fungi, protists, and plants, and that B56 gene duplication prior to the divergence of protostomes and deuterostomes led to the origin of two B56 subfamilies, B56αβε and B56γδ. Further duplications led to three B56αβε genes and two B56γδ in vertebrates. Several nonvertebrate B56 gene names are based on distinct vertebrate isoform names, and would best be renamed. B56 subfamily genes lack significant divergence within primitive chordates, but each became distinct in complex vertebrates. Two vertebrate lineages have undergone B56 gene loss, Xenopus and Aves. In Xenopus, B56δ function may be compensated for by an alternatively spliced transcript, B56δ/γ, encoding a B56δ-like amino-terminal region and a B56γ core.

Evolutionary constraints and expression analysis of gene duplications in Rhodobacter sphaeroides 2.4.1

BackgroundGene duplication is a major force that contributes to the evolution of new metabolic functions in all organisms. Rhodobacter sphaeroides 2.4.1 is a bacterium that displays a wide degree of metabolic versatility and genome complexity and therefore is a fitting model for the study of gene duplications in bacteria. A comprehensive analysis of 234 duplicate gene-pairs in R. sphaeroides was performed using structural constraint and expression analysis.ResultsThe results revealed that most gene-pairs in in-paralogs are maintained under negative selection (ω ≤ 0.3), but the strength of selection differed among in-paralog gene-pairs. Although in-paralogs located on different replicons are maintained under purifying selection, the duplicated genes distributed between the primary chromosome (CI) and the second chromosome (CII) are relatively less selectively constrained than the gene-pairs located within each chromosome. The mRNA expression patterns of duplicate gene-pairs were examined through microarray analysis of this organism grown under seven different growth conditions. Results revealed that ~62% of paralogs have similar expression patterns (cosine ≥ 0.90) over all of these growth conditions, while only ~7% of paralogs are very different in their expression patterns (cosine < 0.50).ConclusionsThe overall findings of the study suggest that only a small proportion of paralogs contribute to the metabolic diversity and the evolution of novel metabolic functions in R. sphaeroides. In addition, the lack of relationships between structural constraints and gene-pair expression suggests that patterns of gene-pair expression are likely associated with conservation or divergence of gene-pair promoter regions and other coregulation mechanisms.

Scalable co-clustering algorithms

Co-clustering has been extensively used in varied applications because of its potential to discover latent local patterns that are otherwise unapparent by usual unsupervised algorithms such as k-means Recently, a unified view of co-clustering algorithms, called Bregman co-clustering (BCC), provides a general framework that even contains several existing co-clustering algorithms, thus we expect to have more applications of this framework to varied data types However, the amount of data collected from real-life application domains easily grows too big to fit in the main memory of a single processor machine Accordingly, enhancing the scalability of BCC can be a critical challenge in practice To address this and eventually enhance its potential for rapid deployment to wider applications with larger data, we parallelize all the twelve co-clustering algorithms in the BCC framework using message passing interface (MPI) In addition, we validate their scalability on eleven synthetic datasets as well as one real-life dataset, where we demonstrate their speedup performance in terms of varied parameter settings.

Detection of JPEG double compression and identification of smartphone image source and post-capture manipulation

Digital multimedia forensics is an emerging field that has important applications in law enforcement and protection of public safety and national security. In digital imaging, JPEG is the most popular lossy compression standard and JPEG images are ubiquitous. Today’s digital techniques make it easy to tamper JPEG images without leaving any visible clues. Furthermore, most image tampering involves JPEG double compression, it heightens the need for accurate analysis of JPEG double compression in image forensics.In this paper, to improve the detection of JPEG double compression, we transplant the neighboring joint density features, which were designed for JPEG steganalysis, and merge the joint density features with marginal density features in DCT domain as the detector for learning classifiers. Experimental results indicate that the proposed method improves the detection performance. We also study the relationship among compression factor, image complexity, and detection accuracy, which has not been comprehensively analyzed before. The results show that a complete evaluation of the detection performance of different algorithms should necessarily include image complexity as well as the double compression quality factor.In addition to JPEG double compression, the identification of image capture source is an interesting topic in image forensics. Mobile handsets are widely used for spontaneous photo capture because they are typically carried by their users at all times. In the imaging device market, smartphone adoption is currently exploding and megapixel smartphones pose a threat to the traditional digital cameras. While smartphone images are widely disseminated, the manipulation of images is also easily performed with various photo editing tools. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. Following the success of our previous work in JPEG double compression detection, we conducted a study to identify smartphone source and post-capture manipulation by utilizing marginal density and neighboring joint density features together. Experimental results show that our method is highly promising for identifying both smartphone source and manipulations.Finally, our study also indicates that applying unsupervised clustering and supervised classification together leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of the intentional post-capture manipulation on smartphone images.

Identification of smartphone-image source and manipulation

As smartphones are being widely used in daily lives, the images captured by smartphones become ubiquitous and may be used for legal purposes. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. In this paper, we propose a method to determine the smartphone camera source of a particular image and operations that may have been performed on that image. We first take images using different smartphones and purposely manipulate the images, including different combinations of double JPEG compression, cropping, and rescaling. Then, we extract the marginal density in low frequency coordinates and neighboring joint density features on intra-block and inter-block as features. Finally, we employ a support vector machine to identify the smartphone source as well as to reveal the operations. Experimental results show that our method is very promising for identifying both smartphone source and manipulations. Our study also indicates that applying unsupervised clustering and supervised classification together (clustering first, followed by classification) leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of intentional manipulation.

The prevalence of gene duplications and their ancient origin in Rhodobacter sphaeroides 2.4.1

BackgroundRhodobacter sphaeroides 2.4.1 is a metabolically versatile organism that belongs to α-3 subdivision of Proteobacteria. The present study was to identify the extent, history, and role of gene duplications in R. sphaeroides 2.4.1, an organism that possesses two chromosomes.ResultsA protein similarity search (BLASTP) identified 1247 orfs (~29.4% of the total protein coding orfs) that are present in 2 or more copies, 37.5% (234 gene-pairs) of which exist in duplicate copies. The distribution of the duplicate gene-pairs in all Clusters of Orthologous Groups (COGs) differed significantly when compared to the COG distribution across the whole genome. Location plots revealed clusters of gene duplications that possessed the same COG classification. Phylogenetic analyses were performed to determine a tree topology predicting either a Type-A or Type-B phylogenetic relationship. A Type-A phylogenetic relationship shows that a copy of the protein-pair matches more with an ortholog from a species closely related to R. sphaeroides while a Type-B relationship predicts the highest match between both copies of the R. sphaeroides protein-pair. The results revealed that ~77% of the proteins exhibited a Type-A phylogenetic relationship demonstrating the ancient origin of these gene duplications. Additional analyses on three other strains of R. sphaeroides revealed varying levels of gene loss and retention in these strains. Also, analyses on common gene pairs among the four strains revealed that these genes experience similar functional constraints and undergo purifying selection.ConclusionsAlthough the results suggest that the level of gene duplication in organisms with complex genome structuring (more than one chromosome) seems to be not markedly different from that in organisms with only a single chromosome, these duplications may have aided in genome reorganization in this group of eubacteria prior to the formation of R. sphaeroides as gene duplications involved in specialized functions might have contributed to complex genomic development.

Data transformation for sum squared residue

The sum squared residue has been popularly used as a clustering and co-clustering quality measure, however little research on its detail properties has been performed. Recent research articulates that the residue is useful to discover shifting patterns but inappropriate to find scaling patterns. To remedy this weakness, we propose to take specific data transformations that can adjust latent scaling factors and eventually lead to lower the residue. First, we consider data matrix models with varied shifting and scaling factors. Then, we formally analyze the effect of several data transformations on the residue. Finally, we empirically validate the analysis with publicly-available human cancer gene expression datasets. Both the analytical and experimental results reveal column standard deviation normalization and column Z-score transformation are effective for the residue to handle scaling factors, through which we are able to achieve better tissue sample clustering accuracy.

Differential Selective Pressures Experienced by the Aurora Kinase Gene Family

Aurora kinases (AKs) are serine/threonine kinases that are essential for cell division. Humans have three AK genes: AKA, AKB, and AKC. AKA is required for centrosome assembly, centrosome separation, and bipolar spindle assembly, and its mutation leads to abnormal spindle morphology. AKB is required for the spindle checkpoint and proper cytokinesis, and mutations cause chromosome misalignment and cytokinesis failure. AKC is expressed in germ cells, and has a role in meiosis analogous to that of AKB in mitosis. Mutation of any of the three isoforms can lead to cancer. AK proteins possess divergent N- and C-termini and a conserved central catalytic domain. We examined the evolution of the AK gene family using an identity matrix and by building a phylogenetic tree. The data suggest that AKA is the vertebrate ancestral gene, and that AKB and AKC resulted from gene duplication in placental mammals. In a nonsynonymous/synonymous rate substitution analysis, we found that AKB experienced the strongest, and AKC the weakest, purifying selection. Both the N- and C-termini and regions within the kinase domain experienced differential selection among the AK isoforms. These differentially selected sequences may be important for species specificity and isoform specificity, and are therefore potential therapeutic targets.

A Nonsynonymous/Synonymous Substitution Analysis of the B56 Gene Family Aids in Understanding B56 Isoform Diversity.

Gene duplication leads to the formation of gene families, wherein purifying or neutral selection maintains the original gene function, while diversifying selection confers new functions onto duplicated genes. The B56 gene family is highly conserved; it is encoded by one gene in protists and fungi, and five genes in vertebrates. B56 regulates protein phosphatase 2A (PP2A), an abundant heterotrimeric serine/threonine phosphatase that functions as a tumor suppressor and consists of a scaffolding “A” and catalytic “C” subunit heterodimer bound to a regulatory “B” subunit. Individual regulatory B56 subunits confer disparate functions onto PP2A in various cell-cell signaling pathways. B56 proteins share a conserved central core domain, but have divergent N- and C-termini which play a role in isoform specificity. We carried out a nonsynonymous/synonymous substitution analysis to better understand the divergence of vertebrate B56 genes. When five B56 paralogs from ten vertebrate species were analyzed, the gene family displayed purifying selection; stronger purifying selection was revealed when individual B56 isoforms were analyzed separately. The B56 core experienced stronger purifying selection than the N- and C-termini, which correlates with the presence of several contacts between the core and the AC heterodimer. Indeed, the majority of the contact points that we analyzed between B56 and the AC heterodimer experienced strong purifying selection. B56 subfamilies showed distinct patterns of selection in their N- and C-termini. The C-terminus of the B56-1 subfamily and the N-terminus of the B56-2 subfamily exhibited strong purifying selection, suggesting that these termini carry out subfamily-specific functions, while the opposite termini exhibited diversifying selection and likely carry out isoform-specific functions. We also found reduced synonymous substitutions at the N- and C-termini when grouping B56 genes by species but not by isoform, suggesting species-specific codon bias may have a role in regulating B56 gene expression.

Situation-Aware on mobile phone using co-clustering: algorithms and extensions

Due to the large number of applications in the mobile phones, users usually go through a fixed menu hierarchy to find a specific interesting application. Hence, in our previous research, we realized the proactive mobile phone application recommendation using co-clustering and demonstrated the promising recommendation performance on a smartphone. The approach first autonomously extracts users behavioral patterns from the usage log of user interactions with the device as well as environments and then recommends potential applications that might be interesting to the user at the corresponding specific situation. In this paper, as a follow-up to this novel platform of intelligent smartphone-based situation-awareness, we investigate sophisticated methodologies that lead to better performance. To achieve this goal, we considered various co-clustering algorithms with different data transformations and weighting schemes for simulated mobile phone usage data. Through non-exhaustive, but pretty comprehensive experimental setting, we find what specific co-clustering algorithms with what specific data transformations and weighting schemes improve accuracy performance in extracting specific user patterns.