5457020
Identifying metabolic enzymes with multiple types of association evidence
BackgroundExisting large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes.ResultsWe present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases.ConclusionWe illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities.
267445
Biological Nomenclatures: A Source of Lexical Knowledge and Ambiguity
There has been increased work in developing automated systems that involve natural language processing (NLP) to recognize and extract genomic information from the literature. Recognition and identification of biological entities is a critical step in this process. NLP systems generally rely on nomenclatures and ontological specifications as resources for determining the names of the entities, assigning semantic categories that are consistent with the corresponding ontology, and assignment of identifiers that map to well-defined entities within a particular nomenclature. Although nomenclatures and ontologies are valuable for text processing systems, they were developed to aid researchers and are heterogeneous in structure and semantics. A uniform resource that is automatically generated from diverse resources, and that is designed for NLP purposes would be a useful tool for the field, and would further database interoperability. This paper presents work towards this goal. We have automatically created lexical resources from four model organism nomenclature systems (mouse, fly, worm, and yeast), and have studied performance of the resources within an existing NLP system, GENIES. Using nomenclatures is not straightforward because issues concerning ambiguity, synonymy, and name variations are quite challenging. In this paper we focus mainly on ambiguity. We determined that the number of ambiguous gene names within the individual nomenclatures, across the four nomenclatures, and with general English ranged from 0%-10.18%, 1.187%-20.30%, and 0%-2.49% respectively. When actually processing text, we found the rate of ambiguous occurrences (not counting ambiguities stemming from English words) to range from 2.4%-32.9% depending on the organisms considered.
3172606
Gene name ambiguity of eukaryotic nomenclatures
MOTIVATION
With more and more scientific literature published online, the effective management and reuse of this knowledge has become problematic. Natural language processing (NLP) may be a potential solution by extracting, structuring and organizing biomedical information in online literature in a timely manner. One essential task is to recognize and identify genomic entities in text. 'Recognition' can be accomplished using pattern matching and machine learning. But for 'identification' these techniques are not adequate. In order to identify genomic entities, NLP needs a comprehensive resource that specifies and classifies genomic entities as they occur in text and that associates them with normalized terms and also unique identifiers so that the extracted entities are well defined. Online organism databases are an excellent resource to create such a lexical resource. However, gene name ambiguity is a serious problem because it affects the appropriate identification of gene entities. In this paper, we explore the extent of the problem and suggest ways to address it.


RESULTS
We obtained gene information from 21 organisms and quantified naming ambiguities within species, across species, with English words and with medical terms. When the case (of letters) was retained, official symbols displayed negligible intra-species ambiguity (0.02%) and modest ambiguities with general English words (0.57%) and medical terms (1.01%). In contrast, the across-species ambiguity was high (14.20%). The inclusion of gene synonyms increased intra-species ambiguity substantially and full names contributed greatly to gene-medical-term ambiguity. A comprehensive lexical resource that covers gene information for the 21 organisms was then created and used to identify gene names by using a straightforward string matching program to process 45,000 abstracts associated with the mouse model organism while ignoring case and gene names that were also English words. We found that 85.1% of correctly retrieved mouse genes were ambiguous with other gene names. When gene names that were also English words were included, 233% additional 'gene' instances were retrieved, most of which were false positives. We also found that authors prefer to use synonyms (74.7%) to official symbols (17.7%) or full names (7.6%) in their publications.


CONTACT
lifeng.chen@dbmi.columbia.edu
2930338
Testing for interconnect crosstalk defects using on-chip embedded processor cores
Crosstalk effects degrade the integrity of signals traveling on long interconnects and must be addressed during production testing. External testing for crosstalk is expensive due to the need for high-speed testers. Built-in self-test, while eliminating the need for a high-speed tester, may lead to excessive test overhead as well as overly aggressive testing. To address this problem, we propose a new software-based self-test methodology for system-on-chip (SoC) devices based on embedded processors. It enables an on-chip embedded processor core to test for crosstalk in system-level interconnects by executing a self-test program in the normal operational mode of the SoC. We have demonstrated the feasibility of this method by applying it to test the interconnects of a processor-memory system. The defect coverage was evaluated using a system-level crosstalk defect simulation method.
26340415
Effects of Filtering Policies on Task Performance in a Desktop CVE System
To quantify the regularity of state updates, the concept of Irregular Degree was introduced. Two experiments corresponding to four different filtering policies were designed. In the first experiment three filtering policies based on Irregular Degree were compared. In the second experiment, the filtering policy achieving the best task performance in the first experiment was selected to compare with other filtering policies. The results of the first experiment indicate that the stability of state updates per second is a more important impact factor than Irregular Degree on the task performance. The results of the second experiment indicate that corresponding to different state updates per second, the filtering policies can achieve different task performance.
31293594
Integrating mRNA Decay Information into Co-Regulation Study
Absolute or relative transcript amounts measured through high-throughput technologies (e.g., microarrays) are now commonly used in bioinformatics analysis, such as gene clustering and DNA binding motif finding. However, transcription rates that represent mRNA synthesis may be more relevant in these analyses. Because transcription rates are not equivalent to transcript amounts unless the mRNA degradation rates as well as other factors that affect transcript amount are identical across different genes, the use of transcription rates in bioinformatics analysis may lead to a better description of the relationships among genes and better identification of genomic signals. In this article, we propose to use experimentally measured mRNA decay rates and mRNA transcript amounts to jointly infer transcription rates, and then use the inferred transcription rates in downstream analyses. For gene expression similarity analysis, we find that there tends to be higher correlations among co-regulated genes when transcription-rate-based correlations are used compared to those based on transcript amounts. In the context of identifying DNA binding motifs, using inferred transcription rates leads to more significant findings than those based on transcript amounts. These analyses suggest that the incorporation of mRNA decay rates and the use of the inferred transcription rates can facilitate the study of gene regulations and the reconstruction of transcriptional regulatory networks.
6316461
Partitioned optimization algorithms for multiple sequence alignment
Multiple sequence alignment is an important and difficult problem in molecular biology and bioinformatics. In this paper, we propose a partitioning approach that significantly improves the solution time and quality by utilizing the locality structure of the problem. The algorithm solves the multiple sequence alignment in three stages. First, an automated and suboptimal partitioning strategy is used to divide the set of sequences into several subsections. Then a multiple sequence alignment algorithm based on ant colony optimization is used to align the sequences of each subsection. Finally, the alignment of original sequences can be obtained by assembling the result of each subsection. The ant colony algorithm is highly optimized in order to avoid local optimal traps and converge to global optimal efficiently. Experimental results show that the algorithm can significantly reduce the running time and improve the solution quality on large-scale multiple sequence alignment benchmarks.
16535706
An efficient architecture for two-dimensional discrete wavelet transform
This paper proposes an efficient architecture for the two-dimensional discrete wavelet transform (2-D DWT). The proposed architecture includes a transform module, a RAM module, and a multiplexer. In the transform module, we employ the polyphase decomposition technique and the coefficient folding technique to the decimation filters of stages 1 and 2, respectively. In comparison with other 2-D DWT architectures, the advantages of the proposed architecture are 100% hardware utilization, fast computing time (0.5-0.67 times of the parallel filters'), regular data flow, and low control complexity, making this architecture suitable for next generation image compression systems, e.g., JPEG-2000.