W H A T

Computing gene expression data with a knowledge-based gene clustering approach

2010-06-17T20:37:00.002+08:00

Computational analysis methods for gene expression data gathered in microarray experiments can be used to identify the functions of previously unstudied genes. While obtaining the expression data is not a difficult task,
interpreting and extracting the information from the datasets is challenging. In this study, a knowledge-based approach which identifies and saves important functional genes before filtering based on variability and fold change differences was utilized to study light regulation. Two clustering methods were used to cluster the filtered datasets, and clusters containing a key light regulatory gene were located. The common genes to both of these clusters were identified, and the genes in the common cluster were ranked based on their coexpression to the key gene. This process was repeated for 11 key genes in 3 treatment combinations. The initial filtering method reduced the dataset size from 22,814 probes to an average of 1134 genes, and the resulting common cluster lists contained an average of only 14 genes. These common cluster lists scored higher gene enrichment scores than two individual clustering methods. In addition, the filtering method increased the proportion of light responsive genes in the dataset from 1.8% to 15.2%, and the cluster lists increased this proportion to 18.4%. The relatively short length of these common cluster lists compared to gene groups generated through typical clustering methods or co-expression networks narrows the search for novel functional genes while increasing the likelihood that they are biologically relevant.

by Bruce A. Rosa, Sookyung Oh, Beronda L. Montgomery, Jin Chen*, Wensheng Qin* (* for corresponding authors), Int J Biochem Mol Biol 2010;1(1):51-68

Exploiting Domain Knowledge to Improve Biological Significance of Bi-clusters with Key Missing Genes

2008-09-24T13:14:00.004+08:00

In an era of increasingly complex biological datasets, one of the key steps in gene functional analysis comes from clustering genes based on co-expression. Biclustering algorithms can identify gene clusters with local co-expressed patterns, which are more likely to define genes functioning together than global clustering methods. However, these algorithms are not effective in uncovering gene regulatory networks because the mined biclusters lack genes that may be critical in the function but may not be co-expressed with the clustered genes. In this project, we introduce a biclustering method called SKeleton Biclustering (SKB), which builds high quality biclusters from microarray data, creates relationships among the biclustered genes based on Gene Ontology annotations, and identifies genes that are missing in the biclusters. SKB thus defines inter-bicluster and intra-bicluster functional relationships. The delineation of functional relationships and incorporation of such missing genes may help biologists to discover biological processes that are important in a given study and provides clues for how the processes may be functioning together. Experimental results on yeast cell cycles and Arabidopsis cold-response microarray datasets show that, with SKB, the biological significance of the biclusters is considerably improved.

--by Jin Chen, Liping Ji, Wynne Hsu, Kian-Lee Tan, Seung Rhee, ICDE, 2009

Molecular and cellular approaches for the detection of protein-protein interactions: Latest Techniques and Current Limitations

2007-09-25T05:47:00.001+08:00

Homo- and heterotypic protein interactions are crucial for all levels of cellular function including architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential components of post-genomic toolkits needed for understanding biological processes at a systems level. Over the past decade, a wide variety of methods have been developed to detect, analyze and quantify protein interactions, including surface plasmon resonance spectroscopy, NMR, yeast two hybrid screens, peptide tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques range from colocalization of tags, which may be limited by the optical resolution of the microscope, to FRET-based methods that have molecular resolution and can also report on the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques described in this review, such as surface plasmon resonance provide detailed information on physical properties of these interactions, while others, such as two hybrid techniques and mass spectrometry, are amenable to high throughput analysis using robotics. In addition to providing an overview of these methods, this review emphasizes techniques that can be applied to determine interactions involving membrane proteins, including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with high throughput approaches. Mass spectrometry-based methods are covered by a review by Thelen et al.. In addition, we discuss the use of interaction data to construct interaction networks and as the basis for the exciting possibility of their usage for predicting interaction surfaces.

--by Sylvie Lalonde, Jin Chen, David W. Ehrhardt, Dominique Loqué, Seung Y. Rhee, Wolf B. Frommer, Plant Journal, 2008

Increasing Confidence of Protein-Protein Interactomes

2007-01-19T06:50:00.000+08:00

High-throughput experimental methods, such as yeast-two-hybrid and phage display, have fairly high levels of false positives (and false negatives). Thus the list of protein-protein interactions detected by such experiments would need additional wet laboratory validation. It would be useful if the list could be prioritized in some way.

Advances in computational techniques for assessing the reliability of protein-protein interactions detected by such high-throughput methods are reviewed in this paper, with a focus on techniques that rely only on topological information of the protein interaction network derived from such high-throughput experiments. In particular, we discuss indices that are abstract mathematical characterizations of networks of reliable protein-protein interactions—e.g., “interaction generality” (IG), “interaction reliability by alternatve pathways” (IRAP), and “functional similarity weighting” (FSWeight). We also present indices that are based on explicit motifs associated with true-positive protein interactions—e.g., “new interaction generality” (IG2) and “meso-scale motifs” (NeMoFinder).

--by Jin Chen, Hon Nian Chua, Wynne Hsu, Mong-Li Lee, See-Kiong Ng, Rintaro Saito, Wing-Kin Sung, Limsoon Wong, GIW keynote, 2006

Labeling network motifs in protein interactomes for protein function prediction

2006-10-13T14:08:00.000+08:00

Biological networks such as the protein-protein interaction (PPI) network have been found to contain small recurring subnetworks in significantly higher frequencies than in random networks. Such network motifs are useful for uncovering structural design principles of complex biological networks. However, current network motif finding algorithms models the PPI network as a uni-labeled graph, discovering only unlabeled and thus relatively uninformative network motifs as a result.

Our objective is to exploit the currently available biological information that are associated with the vertices (the proteins) to capture not only the topological shapes of the motifs, but also the biological context in which they occurred in the PPI networks for network motif applications. We present a method called LaMoFinder to label network motifs with Gene Ontology terms in a PPI network. We also show how the resulting labeled network motifs can be used to predict unknown protein functions. Experimental results showed that the labeled network motifs extracted are biologically meaningful and can achieve better performance than existing PPI topology based methods for predicting unknown protein functions.

--by Jin Chen, Wynne Hsu, Mong Li Lee and See-Kiong Ng, ICDE, 2007

Increasing confidence of protein interactomes using network topological metrics

2006-05-31T11:48:00.000+08:00

Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches.

We describe a novel method called IRAP* as a computational complement for repurification of the highly erroneous experimentally-derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP* using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP* on large-scale interaction data sets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction data sets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity.

--by Jin Chen, Wynne Hsu, Mong Li Lee and See-Kiong Ng, Bioinformatics, 2006

NeMoFinder: Dissecting genome wide protein-protein interactions with repeated and unique network motifs

2006-05-31T11:08:00.000+08:00

Recent works in network analysis have revealed the existence of network motifs in biological networks such as protein-protein interaction (PPI) networks. However, existing motif mining algorithms are not sufficiently scalable to find meso-scale network motifs. Also, there has been little or no work to systematically exploit the extracted network motifs for dissecting the vast interactomes.

We describe an efficient network motif discovery algorithm, NeMoFinder, that can mine meso-scale repeated and unique network motifs in large PPI networks. Using NeMoFinder, we successfully discovered, for the first time, up to size-12 network motifs in a large whole-genome S. cerevisiae (Yeast) PPI network. We also show that such network motifs can be systematically exploited for indexing the reliability of PPI data generated via highly erroneous high-throughput experimental methods.

--by Jin Chen, Wynne Hsu, Mong Li Lee and See-Kiong Ng, SIGKDD, 2006

Assessing Reliability of Protein Interaction Data from High-Throughput Experiments with Belief Inference

2005-07-24T18:20:00.000+08:00

The high false positive rates associated with high-throughput experimental protein interaction data have led researchers to develop methods to assess the reliability of protein interactions generated by large-scale biological experiments.

One approach is to model the expected characteristics of true protein interaction networks, and then devise mathematical measures to assess the reliability of the candidate interactions. Saito et al. developed IG1 and IG2 to assess the reliability in topological way. However, IG1 and IG2 are local measures, which do not consider the topological properties beyond the candidate protein pair and its neighbors. We have recently developed IRAP to assess the reliability of interactions beyond the candidate protein pair with an alternative path approach. As IRAP only considers the strongest alternative path, it may overlook other interactions close to the target pair that may provide significant supporting evidence for their reliability.

We present here a new measure called ‘Interaction Reliability by Belief Inference’ (IRBI) to evaluate protein interactions by considering all the protein interactions close to the target pair. Our validation studies have shown that IRBI is an effective way for detecting reliable protein interactions.

--by Jin Chen, Wynne Hsu, Mong Li Lee, See-Kiong, APBC (poster), 2005.

Discovering Reliable Protein Interactions from High-Throughput Experimental Data using Network Topology

2005-04-05T16:18:00.000+08:00

Current protein-protein interaction(PPI)detection via high-throughput experimental methods such asyeast-two-hybrid has been reported to be highly erroneous, leadingto potentially costly spurious discoveries. This work introduces anovel measure called IRAP, i.e., ''Interaction Reliabilityby Alternative Path'', for assessing the reliability of proteininteractions based on the underlying topology of the PPI network.

A candidate PPI is consideredto be reliable if it is involved in a closed loop in which thealternative path of interactions between the two interactingproteins is strong. We devise an algorithm calledAlternativePathFinder to compute the IRAP value for eachinteraction in a complex PPI network. Validation of the IRAP as ameasure for assessing the reliability of PPIs is performed withextensive experiments on yeast PPI data.

Positive experimental resultsdemonstrate that IRAP is a good measure for assessing thereliability of PPIs from conventional high-throughput experiments.

---by Jin Chen, Wynne Hsu, Mong Li Lee and See-Kiong Ng, Artifical Intelligence in Medicine, 2005

Systematic Assessment of High-Throughput Experimental Data for Reliable Protein Interactions using Network Topology

2004-10-08T17:43:00.001+08:00

Current protein interaction detection via high-throughput experimental methods such as yeast-two-hybrid has been reported to be highly erroneous. This work introduces a novel measure called IRAP for assessing the reliability of protein interaction based on the underlying topology of the protein interaction network.

A candidateprotein interaction is considered to be reliable if it is involved in a closed loop in which the alternative path of interactions between the two interacting proteins is strong. We design an algorithm to compute the IRAP value for each interaction in aprotein interaction network. Validation of IRAP as a measure for assessing the reliability of protein-protein interactions from conventional high-throughput experiments is performed.

We devise aheuristic algorithm to compute IRAP that is able to achieve a 40% speedup in runtime while maintaining a 95% accuracy.

---by Jin Chen, Wynne Hsu, Mong Li Lee and See-Kiong Ng, ICTAI, 2004

Order-Sensitive Clustering for Remote Homologous Protein Detection

2004-10-08T17:38:00.000+08:00

Traditional sequence alignment methods are effectivein identifying homologous proteins that are highly similar. However, these approaches are not able to perform well when they are dealing with remote homologous proteins (proteins whose 3D structures are similar but their sequencesare not). Recent biological research reveals that protein sequences contain residues that determine the 3Dstructure of proteins.

In this work, we investigate incorporating this information to aid in the clustering of proteindatabases. We capture protein residues in the form of patterns with fixed order among them. First, the significant patternsare extracted from the protein sequences. Based on the extracted patterns, we perform sequence mining to generate the order among them. Finally, we adopt a partition-based method to cluster protein sequences using the patterns and order features.

Experiments on COG and SCOP40 datasets show that our new approach is able to generate high quality clusters that are similar to those determined manually by the biologists.

--- by Jin Chen, Wynne Hsu and Mong Li Lee, ICTAI, 2003.