bluuebunny/crossref_metadata_2025_split huggingface dataset:
Protein subcellular localization prediction of eukaryotes using a knowledge-based approach
Authors: Lin Hsin-Nan , Chen Ching-Tai , Sung Ting-Yi , Ho Shinn-Ying , Hsu Wen-Lian 
Published: December 2009

Abstract

Background
The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles.

Results
In this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPredsite's performance. The experiment results show that KnowPredsite achieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPredsite is 91.7%. For multi-localized proteins, the overall accuracy of KnowPredsite is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPredsite.

Conclusion
KnowPredsite demonstrates the power of identifying related sequences in the knowledge base. The experiment results show that even though the sequence similarity is low, the local similarity is effective for prediction. Experiment results show that KnowPredsite is a highly accurate prediction method for both single- and multi-localized proteins. It is worth-mentioning the prediction process of KnowPredsite is transparent and biologically interpretable and it shows a set of template sequences to generate the prediction result. The KnowPredsite prediction server is available at http://bio-cluster.iis.sinica.edu.tw/kbloc/.
MapNext: a software tool for spliced and unspliced alignments and SNP detection of short sequence reads
Authors: Bao Hua , Xiong Yuanyan , Guo Hui , Zhou Renchao , Lu Xuemei , Yang Zhen , Zhong Yang , Shi Suhua 
Published: December 2009

Abstract

Background
Next-generation sequencing technologies provide exciting avenues for studies of transcriptomics and population genomics. There is an increasing need to conduct spliced and unspliced alignments of short transcript reads onto a reference genome and estimate minor allele frequency from sequences of population samples.

Results
We have designed and implemented MapNext, a software tool for both spliced and unspliced alignments of short sequence reads onto reference sequences, and automated SNP detection using neighbourhood quality standards. MapNext provides four main analyses: (i) unspliced alignment and clustering of reads, (ii) spliced alignment of transcript reads over intron boundaries, (iii) SNP detection and estimation of minor allele frequency from population sequences, and (iv) storage of result data in a database to make it available for more flexible queries and for further analyses. The software tool has been tested using both simulated and real data.

Conclusion
MapNext is a comprehensive and powerful tool for both spliced and unspliced alignments of short reads and automated SNP detection from population sequences. The simplicity, flexibility and efficiency of MapNext makes it a valuable tool for transcriptomic and population genomic research.
MINER: exploratory analysis of gene interaction networks by machine learning from expression data
Authors: Kadupitige Sidath Randeni , Leung Kin Chun , Sellmeier Julia , Sivieng Jane , Catchpoole Daniel R , Bain Michael E , Gaëta Bruno A 
Published: December 2009

Abstract

Background
The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies.

Results
We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation.

Conclusion
Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.
PutidaNET: Interactome database service and network analysis of Pseudomonas putida KT2440
Authors: Park Seong-Jin , Choi Jong-Soon , Kim Byoung-Chul , Jho Seong-Woong , Ryu Jea-Woon , Park Daeui , Lee Kyung-A , Bhak Jong , Kim Seung Il 
Published: December 2009

Abstract

Background

Pseudomonas putida KT2440 (P. putida KT2440) is a highly versatile saprophytic soil bacterium. It is a certified bio-safety host for transferring foreign genes. Therefore, the bacterium is used as a model organism for genetic and physiological studies and for the development of biotechnological applications. In order to provide a more systematic application of the organism, we have constructed a protein-protein interaction (PPI) network analysis system of P. putida KT2440.

Results
PutidaNET is a comprehensive interaction database and server of P. putida KT2440 which is generated from three protein-protein interaction (PPI) methods. We used PSIMAP (Protein Structural Interactome MAP), PEIMAP (Protein Experimental Interactome MAP), and Domain-domain interactions using iPfam. PutidaNET contains 3,254 proteins, and 82,019 possible interactions consisting of 61,011 (PSIMAP), 4,293 (PEIMAP), and 30,043 (iPfam) interaction pairs except for self interaction. Also, we performed a case study by integrating a protein interaction network and experimental 1-DE/MS-MS analysis data P. putida. We found that 1) major functional modules are involved in various metabolic pathways and ribosomes, and 2) existing PPI sub-networks that are specific to succinate or benzoate metabolism are not in the center as predicted.

Conclusion
We introduce the PutidaNET which provides predicted interaction partners and functional analyses such as physicochemical properties, KEGG pathway assignment, and Gene Ontology mapping of P. putida KT2440 PutidaNET is freely available at http://sequenceome.kobic.kr/PutidaNET.
Comparative study of protein-protein interaction observed in PolyGalacturonase-Inhibiting Proteins from Phaseolus vulgaris and Glycine max and PolyGalacturonase from Fusarium moniliforme
Authors: Maulik Aditi , Ghosh Hiren , Basu Soumalee 
Published: December 2009

Abstract

Background
The PolyGalacturonase-Inhibiting Proteins (PGIP) of plant cell wall limit the invasion of phytopathogenic organisms by interacting with the enzyme PolyGalacturonase (PG) they secrete to degrade pectin present in the cell walls. PGIPs from different or same plant differ in their inhibitory activity towards the same PG. PGIP2 from Phaseolus vulgaris (Pv) inhibits the PG from Fusarium moniliforme (Fm) although PGIP1, another member of the multigene family from the same plant sharing 99% sequence similarity, cannot. Interestingly, PGIP3 from Glycine max (Gm) which is a homologue of PGIP2 is capable of inhibiting the same PG although the extent of similarity is lower and is 88%. It therefore appears that subtle changes in the sequence of plant PGIPs give rise to different specificity for inhibiting pathogenic PGs and there exists no direct dependence of function on the extent of sequence similarity.

Results
Structural information for any PGIP-PG complex being absent, we resorted to molecular modelling to gain insight into the mechanism of recognition and discrimination of PGs by PGIPs. We have built homology models of Pv PGIP1 and Gm PGIP3 using the crystal structure of Pv PGIP2 (1OGQ) as template. These PGIPs were then docked individually to Fm PG to elucidate the characteristics of their interactions. The mode of binding for Pv PGIP1 to Fm PG considerably differs from the mode observed for Pv PGIP2-Fm PG complex, regardless of the high sequence similarity the two PGIPs share. Both Pv PGIP2 and Gm PGIP3 despite being relatively less similar, interact with residues of Fm PG that are known from mutational studies to constitute the active site of the enzyme. Pv PGIP1 tends to interact with residues not located at the active site of Fm PG. Looking into the electrostatic potential surface for individual PGIPs, it was evident that a portion of the interacting surface for Pv PGIP1 differs from the corresponding region of Pv PGIP2 or Gm PGIP3.

Conclusion
van der Waals and eletrostatic interactions play an active role in PGIPs for proper recognition and discrimination of PGs. Docking studies reveal that Pv PGIP2 and Gm PGIP3 interact with the residues constituting the active site of Fm PG with implications that the proteins bind/block Fm PG at its active site and thereby inhibit the enzyme.
A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins
Authors: Liu Yu-Cheng , Yang Meng-Han , Lin Win-Li , Huang Chien-Kang , Oyang Yen-Jen 
Published: December 2009

Abstract

Background
Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research.

Results
In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode.

Conclusion
Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.
Identification of cell cycle-related regulatory motifs using a kernel canonical correlation analysis
Authors: Rhee Je-Keun , Joung Je-Gun , Chang Jeong-Ho , Fei Zhangjun , Zhang Byoung-Tak 
Published: December 2009

Abstract

Background
Gene regulation is a key mechanism in higher eukaryotic cellular processes. One of the major challenges in gene regulation studies is to identify regulators affecting the expression of their target genes in specific biological processes. Despite their importance, regulators involved in diverse biological processes still remain largely unrevealed. In the present study, we propose a kernel-based approach to efficiently identify core regulatory elements involved in specific biological processes using gene expression profiles.

Results
We developed a framework that can detect correlations between gene expression profiles and the upstream sequences on the basis of the kernel canonical correlation analysis (kernel CCA). Using a yeast cell cycle dataset, we demonstrated that upstream sequence patterns were closely related to gene expression profiles based on the canonical correlation scores obtained by measuring the correlation between them. Our results showed that the cell cycle-specific regulatory motifs could be found successfully based on the motif weights derived through kernel CCA. Furthermore, we identified co-regulatory motif pairs using the same framework.

Conclusion
Given expression profiles, our method was able to identify regulatory motifs involved in specific biological processes. The method could be applied to the elucidation of the unknown regulatory mechanisms associated with complex gene regulatory processes.
Alternative paths in HIV-1 targeted human signal transduction pathways
Authors: Balakrishnan Sivaraman , Tastan Oznur , Carbonell Jaime , Klein-Seetharaman Judith 
Published: December 2009

AbstractBackgroundHuman immunodeficiency virus-1 (HIV-1) has a minimal genome of only 9 genes, which encode 15 proteins. HIV-1 thus depends on the human host for virtually every aspect of its life cycle. The universal language of communication in biological systems, including between pathogen and host, is via signal transduction pathways. The fundamental units of these pathways are protein protein interactions. Understanding the functional significance of HIV-1, human interactions requires viewing them in the context of human signal transduction pathways.ResultsIntegration of HIV-1, human interactions with known signal transduction pathways indicates that the majority of known human pathways have the potential to be effected through at least one interaction with an HIV-1 protein at some point during the HIV-1 life cycle. For each pathway, we define simple paths between start points (i.e. no edges going into a node) and end points (i.e. no edges leaving a node). We then identify the paths that pass through human proteins that interact with HIV-1 proteins. We supplement the combined map with functional information, including which proteins are known drug targets and which proteins contribute significantly to HIV-1 function as revealed by recent siRNA screens. We find that there are often alternative paths starting and ending at the same proteins but circumventing the intermediate steps disrupted by HIV-1.ConclusionA mapping of HIV-1, human interactions to human signal transduction pathways is presented here to link interactions with functions. We proposed a new way of analyzing the virus host interactions by identifying HIV-1 targets as well as alternative paths bypassing the HIV-1 targeted steps. This approach yields numerous experimentally testable hypotheses on how HIV-1 function may be compromised and human cellular function restored by pharmacological approaches. We are making the full set of pathway analysis results available to the community.
COMUS: Clinician-Oriented locus-specific MUtation detection and deposition System
Authors: Jho Sungwoong , Kim Byoung-Chul , Ghang Ho , Kim Ji-Han , Park Daeui , Kim Hak-Min , Jung Soo-young , Yoo Ki-young , Kim Hee-Jin , Lee Sunghoon , Bhak Jong 
Published: December 2009

Abstract

Background
A disease-causing mutation refers to a heritable genetic change that is associated with a specific phenotype (disease). The detection of a mutation from a patient's sample is critical for the diagnosis, treatment, and prognosis of the disease. There are numerous databases and applications with which to archive mutation data. However, none of them have been implemented with any automated bioinformatics tools for mutation detection and analysis starting from raw data materials from patients. We present a Locus Specific mutation DB (LSDB) construction system that supports both mutation detection and deposition in one package.

Results
COMUS (Clinician-Oriented locus specific MUtation detection and deposition System) is a mutation detection and deposition system for developing specific LSDBs. COMUS contains 1) a DNA sequence mutation analysis method for clinicians' mutation data identification and deposition and 2) a curation system for variation detection from clinicians' input data. To embody the COMUS system and to validate its clinical utility, we have chosen the disease hemophilia as a test database. A set of data files from bench experiments and clinical information from hemophilia patients were tested on the LSDB, KoHemGene http://www.kohemgene.org, which has proven to be a clinician-friendly interface for mutation detection and deposition.

Conclusion
COMUS is a bioinformatics system for detecting and depositing new mutations from patient DNA with a clinician-friendly interface. LSDBs made using COMUS will promote the clinical utility of LSDBs. COMUS is available at http://www.comus.info.
BioBarcode: a general DNA barcoding database and server platform for Asian biodiversity resources
Authors: Lim Jeongheui , Kim Sang-Yoon , Kim Sungmin , Eo Hae-Seok , Kim Chang-Bae , Paek Woon Kee , Kim Won , Bhak Jong 
Published: December 2009

AbstractBackgroundDNA barcoding provides a rapid, accurate, and standardized method for species-level identification using short DNA sequences. Such a standardized identification method is useful for mapping all the species on Earth, particularly when DNA sequencing technology is cheaply available. There are many nations in Asia with many biodiversity resources that need to be mapped and registered in databases.ResultsWe have built a general DNA barcode data processing system, BioBarcode, with open source software - which is a general purpose database and server. It uses mySQL RDBMS 5.0, BLAST2, and Apache httpd server. An exemplary database of BioBarcode has around 11,300 specimen entries (including GenBank data) and registers the biological species to map their genetic relationships. The BioBarcode database contains a chromatogram viewer which improves the performance in DNA sequence analyses.ConclusionAsia has a very high degree of biodiversity and the BioBarcode database server system aims to provide an efficient bioinformatics protocol that can be freely used by Asian researchers and research organizations interested in DNA barcoding. The BioBarcode promotes the rapid acquisition of biological species DNA sequence data that meet global standards by providing specialized services, and provides useful tools that will make barcoding cheaper and faster in the biodiversity community such as standardization, depository, management, and analysis of DNA barcode data. The system can be downloaded upon request, and an exemplary server has been constructed with which to build an Asian biodiversity systemhttp://www.asianbarcode.org.
Identification of membrane-type 1 matrix metalloproteinase tyrosine phosphorylation in association with neuroblastoma progression
Authors: Nyalendo Carine , Sartelet Hervé , Barrette Stéphane , Ohta Shigeru , Gingras Denis , Béliveau Richard 
Published: December 2009

Abstract

Background
Neuroblastoma is a pediatric tumor of neural crest cells that is clinically characterized by its variable evolution, from spontaneous regression to malignancy. Despite many advances in neuroblastoma research, 60% of neuroblastoma, which are essentially metastatic cases, are associated with poor clinical outcome due to the lack of effectiveness of current therapeutic strategies. Membrane-type 1 matrix metalloproteinase (MT1-MMP, MMP-14), an enzyme involved in several steps in tumor progression, has previously been shown to be associated with poor clinical outcome for neuroblastoma. Based on our recent demonstration that MT1-MMP phosphorylation is involved in the growth of fibrosarcoma tumors, we examined the potential role of phosphorylated MT1-MMP in neuroblastoma progression.

Methods
Tyrosine phosphorylated MT1-MMP was immunostained on tissue microarray samples from 55 patients with neuroblastoma detected by mass screening (known to be predominantly associated with favourable outcome), and from 234 patients with standard diagnosed neuroblastoma. In addition, the effects of a non phosphorylable version of MT1-MMP on neuroblastoma cell migration and proliferation were investigated within three-dimensional collagen matrices.

Results
Although there is no correlation between the extent of tyrosine phosphorylation of MT1-MMP (pMT1-MMP) and MYCN amplification or clinical stage, we observed greater phosphorylation of pMT1-MMP in standard neuroblastoma, while it is less evident in neuroblastoma from mass screening samples (P = 0.0006) or in neuroblastoma samples from patients younger than one year (P = 0.0002). In vitro experiments showed that overexpression of a non-phosphorylable version of MT1-MMP reduced MT1-MMP-mediated neuroblastoma cell migration and proliferation within a three-dimensional type I collagen matrix, suggesting a role for the phosphorylated enzyme in the invasive properties of neuroblastoma cells.

Conclusion
Overall, these results suggest that tyrosine phosphorylated MT1-MMP plays an important role in neuroblastoma progression and that its expression is preferentially observed in tumor specimens from neuroblastoma patients showing poor clinical outcome.