Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0006
C. Li, W. Chang, B. S. Chen
The circadian regulatory network is one of the main topics of plant investigations. The intracellular interactions among genes in response to the environmental stimuli of light are related to the foundation of functional genomics in plant. However, the sensitivity analysis of the circadian system has not analyzed by perturbed stochastic dynamic model via microarray data in plant. In this study, the circadian network is constructed for Arabidopsis thaliana using a stochastic dynamic model with sigmoid interaction, activation delay, and regulation of input light taken into consideration. The describing function method in nonlinear control theory about nonlinear limit cycle (oscillation) is employed to interpret the oscillations of the circadian regulatory networks from the viewpoint that nonlinear network will continue to oscillate if its feedback loop gain is equal to 1 to support the oscillation of circadian network. Based on the dynamic model via microarray data, the system sensitivity analysis is performed to assess the robustness of circadian regulatory network via biological perturbations. We found that the circadian network is more sensitive to the perturbation of the trans-expression threshold, is more sensitive to the activation level of steady state, rather than the trans-sensitivity rate.
{"title":"System Identification and Robustness Analysis of the Circadian Regulatory Network via Microarray Data in Arabidopsis Thaliana","authors":"C. Li, W. Chang, B. S. Chen","doi":"10.1142/9781860947292_0006","DOIUrl":"https://doi.org/10.1142/9781860947292_0006","url":null,"abstract":"The circadian regulatory network is one of the main topics of plant investigations. The intracellular interactions among genes in response to the environmental stimuli of light are related to the foundation of functional genomics in plant. However, the sensitivity analysis of the circadian system has not analyzed by perturbed stochastic dynamic model via microarray data in plant. In this study, the circadian network is constructed for Arabidopsis thaliana using a stochastic dynamic model with sigmoid interaction, activation delay, and regulation of input light taken into consideration. The describing function method in nonlinear control theory about nonlinear limit cycle (oscillation) is employed to interpret the oscillations of the circadian regulatory networks from the viewpoint that nonlinear network will continue to oscillate if its feedback loop gain is equal to 1 to support the oscillation of circadian network. Based on the dynamic model via microarray data, the system sensitivity analysis is performed to assess the robustness of circadian regulatory network via biological perturbations. We found that the circadian network is more sensitive to the perturbation of the trans-expression threshold, is more sensitive to the activation level of steady state, rather than the trans-sensitivity rate.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"76 1","pages":"27-37"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80584103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0030
L. Yang, W. Hsu, M. Lee, L. Wong
MiRNAs are short non-coding RNAs that regulate gene expression. While the first miRNAs were discovered using experimental methods, experimental miRNA identification remains technically challenging and incomplete. This calls for the development of computational approaches to complement experimental approaches to miRNA gene identification. We pr opose in this paper a de novo miRNA precursor prediction method. This method follows the “feature generation, feature selection, and feature integration” paradigm of constructing recognition models for genomics sequences. We generate and identified features based on information in both primary sequence and secondary structure, and use these features to construct SVM-based models for the recognition of miRNA precursors. Experimental results show that our method is effective, and can achieve good sensitivity and specificity.
{"title":"Identification of MicroRNA Precursors via SVM","authors":"L. Yang, W. Hsu, M. Lee, L. Wong","doi":"10.1142/9781860947292_0030","DOIUrl":"https://doi.org/10.1142/9781860947292_0030","url":null,"abstract":"MiRNAs are short non-coding RNAs that regulate gene expression. While the first miRNAs were discovered using experimental methods, experimental miRNA identification remains technically challenging and incomplete. This calls for the development of computational approaches to complement experimental approaches to miRNA gene identification. We pr opose in this paper a de novo miRNA precursor prediction method. This method follows the “feature generation, feature selection, and feature integration” paradigm of constructing recognition models for genomics sequences. We generate and identified features based on information in both primary sequence and secondary structure, and use these features to construct SVM-based models for the recognition of miRNA precursors. Experimental results show that our method is effective, and can achieve good sensitivity and specificity.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"33 1","pages":"267-276"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82424268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0002
M. Ragan
Most genes have attained their observed distribution among ge omes by transmission from parent to offspring through time. In prokaryotes (bacteria and archa ea), however, some genes are where they are as the result of transfer from an unrelated lineage. To el ucidate the biological origins and functional consequences of lateral gene transfer (LGT), we have constructed an automated computational pipeline to recognise protein families among prokaryotic g enomes, generate high-quality multiple sequence alignments of orthologs, infer statistically sound phylogenetic trees, and find topologically incongruent subtrees (prima facie instances of LGT). This pip eline requires that we automate workflows, design and optimize algorithms, mobilise high-performanc e computing resources, and efficiently manage federated data. I will summarise results from the automa ted comparison of 422971 proteins in 22437 families across 144 sequenced prokaryotic genomes, i nclud ng the nature and extent of LGT among these lineages, major donors and recipients, the bioc hemi al pathways and physiological functions most affected, and implications for the role of LGT in e volution of biochemical pathways.
{"title":"Automating the Search for Lateral Gene Transfer","authors":"M. Ragan","doi":"10.1142/9781860947292_0002","DOIUrl":"https://doi.org/10.1142/9781860947292_0002","url":null,"abstract":"Most genes have attained their observed distribution among ge omes by transmission from parent to offspring through time. In prokaryotes (bacteria and archa ea), however, some genes are where they are as the result of transfer from an unrelated lineage. To el ucidate the biological origins and functional consequences of lateral gene transfer (LGT), we have constructed an automated computational pipeline to recognise protein families among prokaryotic g enomes, generate high-quality multiple sequence alignments of orthologs, infer statistically sound phylogenetic trees, and find topologically incongruent subtrees (prima facie instances of LGT). This pip eline requires that we automate workflows, design and optimize algorithms, mobilise high-performanc e computing resources, and efficiently manage federated data. I will summarise results from the automa ted comparison of 422971 proteins in 22437 families across 144 sequenced prokaryotic genomes, i nclud ng the nature and extent of LGT among these lineages, major donors and recipients, the bioc hemi al pathways and physiological functions most affected, and implications for the role of LGT in e volution of biochemical pathways.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"54 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80408853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0020
Steinar Thorvaldsen, E. Ytterstad, T. Flå
Multiple sequence alignments can provide information for comparative analyses of proteins and protein populations. We present some statistical trend-tests that can be used when an aligned data set can be divided into two or more populations based on phenotypic traits such as preference of temperature, pH, salt concentration or pressure. The approach is based on estimation and analysis of the variation between the values of physicochemical parameters at positions of the sequence alignment. Monotonic trends are detected by applying a cumulative Mann-Kendall test. The method is found to be useful to identify significant physicochemical mechanisms behind adaptation to extreme environments and uncover molecular differences between mesophile and extremophile organisms. A filtering technique is also presented to visualize the underlying structure in the data. All the comparative statistical methods are available in the toolbox DeltaProt.
{"title":"Property-Dependent Analysis of Aligned Proteins from Two Or More Populations","authors":"Steinar Thorvaldsen, E. Ytterstad, T. Flå","doi":"10.1142/9781860947292_0020","DOIUrl":"https://doi.org/10.1142/9781860947292_0020","url":null,"abstract":"Multiple sequence alignments can provide information for comparative analyses of proteins and protein populations. We present some statistical trend-tests that can be used when an aligned data set can be divided into two or more populations based on phenotypic traits such as preference of temperature, pH, salt concentration or pressure. The approach is based on estimation and analysis of the variation between the values of physicochemical parameters at positions of the sequence alignment. Monotonic trends are detected by applying a cumulative Mann-Kendall test. The method is found to be useful to identify significant physicochemical mechanisms behind adaptation to extreme environments and uncover molecular differences between mesophile and extremophile organisms. A filtering technique is also presented to visualize the underlying structure in the data. All the comparative statistical methods are available in the toolbox DeltaProt.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"31 1","pages":"169-178"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87606057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0018
Sandip Paul, Sabyasachi Das, C. Dutta
Trends in synonymous codon usage in adenoviruses have been examined through the multivariate statistical analysis on the annotated protein-coding regions of 22 adenoviral species, for which complete genome sequences are available. One of the major determinants of such trends is the G + C content at third codon positions of the genes, the average value of which varied from one viral genome to other depending on the overall mutational bias of the species. G3S and C3S interacted synergistically along the first principal axis of Correspondence analysis on the Relative Synonymous Codon Usage of adenoviral genes, but antagonistically along the second principal axis. Other major determinants of the trends are the natural selection, putatively operative at the level of translation and quite interestingly, hydropathy of the encoded proteins. The trends in codon usage, though characterized by distinct virus-specific mutational bias, do not exhibit any sign of host-specificity. Significant variations are observed in synonymous codon choice in structural and nonstructural genes of adenoviruses.
{"title":"Consequences of Mutation, Selection, and Physico-Chemical Properties of Encoded Proteins on Synonymous Codon Usage in Adenoviruses","authors":"Sandip Paul, Sabyasachi Das, C. Dutta","doi":"10.1142/9781860947292_0018","DOIUrl":"https://doi.org/10.1142/9781860947292_0018","url":null,"abstract":"Trends in synonymous codon usage in adenoviruses have been examined through the multivariate statistical analysis on the annotated protein-coding regions of 22 adenoviral species, for which complete genome sequences are available. One of the major determinants of such trends is the G + C content at third codon positions of the genes, the average value of which varied from one viral genome to other depending on the overall mutational bias of the species. G3S and C3S interacted synergistically along the first principal axis of Correspondence analysis on the Relative Synonymous Codon Usage of adenoviral genes, but antagonistically along the second principal axis. Other major determinants of the trends are the natural selection, putatively operative at the level of translation and quite interestingly, hydropathy of the encoded proteins. The trends in codon usage, though characterized by distinct virus-specific mutational bias, do not exhibit any sign of host-specificity. Significant variations are observed in synonymous codon choice in structural and nonstructural genes of adenoviruses.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"17 1","pages":"149-158"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82581336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0028
S. Huang, Debra L. Fulton, David J. Arenillas, P. Perco, S. Sui, J. Mortimer, W. Wasserman
Transcription regulation is mediated by combinatorial interactions between diverse trans-acting proteins and arrays of cis-regulatory sequences. Revealing this complex interplay between transcription factors and binding sites remains a fundamental problem for understanding the flow of genetic information. The oPOSSUM analysis system facilitates the interpretation of gene expression data through the analysis of transcription factor binding sites shared by sets of co-expressed genes. The system is based on cross-species sequence comparisons for phylogenetic footprinting and motif models for binding site prediction. We introduce a new set of analysis algorithms for the study of the combinatorial properties of transcription factor binding sites shared by sets of co-expressed genes. The new methods circumvent computational challenges through an applied focus on families of transcription factors with similar binding properties. The algorithm accurately identifies combinations of binding sites over-represented in reference collections and clarifies the results obtained by existing methods for the study of isolated binding sites.
{"title":"Identification of Over-Represented Combinations of Transcription Factor Binding Sites in Sets of Co-Expressed Genes","authors":"S. Huang, Debra L. Fulton, David J. Arenillas, P. Perco, S. Sui, J. Mortimer, W. Wasserman","doi":"10.1142/9781860947292_0028","DOIUrl":"https://doi.org/10.1142/9781860947292_0028","url":null,"abstract":"Transcription regulation is mediated by combinatorial interactions between diverse trans-acting proteins and arrays of cis-regulatory sequences. Revealing this complex interplay between transcription factors and binding sites remains a fundamental problem for understanding the flow of genetic information. The oPOSSUM analysis system facilitates the interpretation of gene expression data through the analysis of transcription factor binding sites shared by sets of co-expressed genes. The system is based on cross-species sequence comparisons for phylogenetic footprinting and motif models for binding site prediction. We introduce a new set of analysis algorithms for the study of the combinatorial properties of transcription factor binding sites shared by sets of co-expressed genes. The new methods circumvent computational challenges through an applied focus on families of transcription factors with similar binding properties. The algorithm accurately identifies combinations of binding sites over-represented in reference collections and clarifies the results obtained by existing methods for the study of isolated binding sites.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"53 1","pages":"247-256"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75677973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0031
M. Shashikanth, A. Snethalatharani, S. Mubarak, K. Ulaganathan
Genome-wide computational analysis for small nuclear RNA (snRNA) genes resulted in identification of 76 and 73 putative snRNA genes from indica and japonica rice genomes, respectively. We used the basic criteria of a minimum of 70 % sequence identity to the plant snRNA gene used for genome search, presence of conserved promoter elements: TATA box, USE motif and monocot promoter specific elements (MSPs) and extensive sequence alignment to rice / plant expressed sequence tags to denote predicted sequence as snRNA genes. Comparative sequence analysis with snRNA genes from other organisms and predicted secondary structures showed that there is overall conservation of snRNA sequence and structure with plant specific features (presence of TATA box in both polymerase II and III transcribed genes, location of USE motif upstream to the TATA box at fixed but different distance in polymerase II and polymerase III transcribed snRNA genes) and the presence of multiple monocot specific MSPs upstream to the USE motif. Detailed analysis results including all multiple sequence alignments, sequence logos, secondary structures, sequences etc are available at http://kulab.org
对小核RNA (snRNA)基因进行全基因组计算分析，分别从籼稻和粳稻基因组中鉴定出76个和73个推测的snRNA基因。我们使用了与植物snRNA基因至少70%序列一致性的基本标准，用于基因组搜索，存在保守启动子元件:TATA box, USE motif和单子叶启动子特异性元件(MSPs)，以及与水稻/植物表达序列标签广泛的序列比对，以表示预测序列为snRNA基因。与其他生物的snRNA基因和预测的二级结构的序列比较分析表明，snRNA序列和结构总体上具有植物特异性特征(在聚合酶II和聚合酶III转录的基因中都存在TATA box，在聚合酶II和聚合酶III转录的snRNA基因中，USE基序在TATA box上游的位置固定但距离不同)，并且在USE基序上游存在多个单株特异性MSPs。详细的分析结果包括所有多个序列比对，序列标识，二级结构，序列等可在http://kulab.org上获得
{"title":"Genome Wide Computational Analysis of Small Nuclear RNA Genes for Oryza Sativa (Indica and Japonica)","authors":"M. Shashikanth, A. Snethalatharani, S. Mubarak, K. Ulaganathan","doi":"10.1142/9781860947292_0031","DOIUrl":"https://doi.org/10.1142/9781860947292_0031","url":null,"abstract":"Genome-wide computational analysis for small nuclear RNA (snRNA) genes resulted in identification of 76 and 73 putative snRNA genes from indica and japonica rice genomes, respectively. We used the basic criteria of a minimum of 70 % sequence identity to the plant snRNA gene used for genome search, presence of conserved promoter elements: TATA box, USE motif and monocot promoter specific elements (MSPs) and extensive sequence alignment to rice / plant expressed sequence tags to denote predicted sequence as snRNA genes. Comparative sequence analysis with snRNA genes from other organisms and predicted secondary structures showed that there is overall conservation of snRNA sequence and structure with plant specific features (presence of TATA box in both polymerase II and III transcribed genes, location of USE motif upstream to the TATA box at fixed but different distance in polymerase II and polymerase III transcribed snRNA genes) and the presence of multiple monocot specific MSPs upstream to the USE motif. Detailed analysis results including all multiple sequence alignments, sequence logos, secondary structures, sequences etc are available at http://kulab.org","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"99 2","pages":"277-286"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91443933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0015
Yifei Ma, Guoren Wang, Yongguang Li, Yuhai Zhao
Finding motifs in DNA sequences plays an important role in deciphering transcriptional regulatory mechanisms and drug target identification. In this paper, we propose an efficient algorithm, EDAM, for finding motifs based on frequency transformation and Minimum Bounding Rectangle (MBR) techniques. It works in three phases, frequency transformation, MBR-clique searching and motif discovery. In frequency transformation, EDAM divides the sample sequences into a set of substrings by sliding windows, then transforms them to frequency vectors which are stored in MBRs. In MBR-clique searching, based on the frequency distance theorems EDAM searches for MBR-cliques used for motif discovery. In motif discovery, EDAM discovers larger cliques by extending smaller cliques with their neighbors. To accelerate the clique discovery, we propose a range query facility to avoid unnecessary computations for clique extension. The experimental results illustrate that EDAM well solves the running time bottleneck of the motif discovery problem in large DNA database.
{"title":"EDAM: An Efficient Clique Discovery Algorithm with Frequency Transformation for Finding Motifs","authors":"Yifei Ma, Guoren Wang, Yongguang Li, Yuhai Zhao","doi":"10.1142/9781860947292_0015","DOIUrl":"https://doi.org/10.1142/9781860947292_0015","url":null,"abstract":"Finding motifs in DNA sequences plays an important role in deciphering transcriptional regulatory mechanisms and drug target identification. In this paper, we propose an efficient algorithm, EDAM, for finding motifs based on frequency transformation and Minimum Bounding Rectangle (MBR) techniques. It works in three phases, frequency transformation, MBR-clique searching and motif discovery. In frequency transformation, EDAM divides the sample sequences into a set of substrings by sliding windows, then transforms them to frequency vectors which are stored in MBRs. In MBR-clique searching, based on the frequency distance theorems EDAM searches for MBR-cliques used for motif discovery. In motif discovery, EDAM discovers larger cliques by extending smaller cliques with their neighbors. To accelerate the clique discovery, we propose a range query facility to avoid unnecessary computations for clique extension. The experimental results illustrate that EDAM well solves the running time bottleneck of the motif discovery problem in large DNA database.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"33 1","pages":"119-128"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79263502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0038
De Pan, Fei Wang
Clustering is widely used in gene expression analysis, which helps to group genes with similar biological function together. The traditional clustering techniques are not suitable to be directly applied to gene expression time series data, because of the inhered properties of local regulation and time shift. In order to cope with the existing problems, the local similarity and time shift, we have developed a new similarity measurement technique called Local Similarity Combination in this paper. And at last, we’ll run our method on the real gene expression data and show that it works well.
{"title":"Gene Expression Data Clustering Based on Local Similarity Combination","authors":"De Pan, Fei Wang","doi":"10.1142/9781860947292_0038","DOIUrl":"https://doi.org/10.1142/9781860947292_0038","url":null,"abstract":"Clustering is widely used in gene expression analysis, which helps to group genes with similar biological function together. The traditional clustering techniques are not suitable to be directly applied to gene expression time series data, because of the inhered properties of local regulation and time shift. In order to cope with the existing problems, the local similarity and time shift, we have developed a new similarity measurement technique called Local Similarity Combination in this paper. And at last, we’ll run our method on the real gene expression data and show that it works well.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"45 1","pages":"353-362"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75680433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-12-01DOI: 10.1142/9781860947292_0016
M. Ng
Multiple loci analysis has become popular with the advanced development in biological experiments. A lot of studies have been focused on the biological and the statistical properties of such multiple loci analysis. In this paper, we study one of the important computational problems: solving the probabilities of haplotype classes from a large linear system Ax = b derived from the recombination events in multiple loci analysis. Since the size of the recombination matrix A increases exponentially with respect to the number of loci, fast solvers are required to deal with a large number of loci in the analysis. By exploiting the nice structure of the matrix A, we develop an efficient recursive algorithm for solving such structured linear systems. In particular, the complexity of the proposed algorithm is of O(mlogm) operations and the memory requirement is of O(m) locations where m is the size of the matrix A. Numerical examples are given to demonstrate the effectiveness of our efficient solver.
{"title":"A Recursive Method for Solving Haplotype Frequencies in Multiple Loci Linkage Analysis","authors":"M. Ng","doi":"10.1142/9781860947292_0016","DOIUrl":"https://doi.org/10.1142/9781860947292_0016","url":null,"abstract":"Multiple loci analysis has become popular with the advanced development in biological experiments. A lot of studies have been focused on the biological and the statistical properties of such multiple loci analysis. In this paper, we study one of the important computational problems: solving the probabilities of haplotype classes from a large linear system Ax = b derived from the recombination events in multiple loci analysis. Since the size of the recombination matrix A increases exponentially with respect to the number of loci, fast solvers are required to deal with a large number of loci in the analysis. By exploiting the nice structure of the matrix A, we develop an efficient recursive algorithm for solving such structured linear systems. In particular, the complexity of the proposed algorithm is of O(mlogm) operations and the memory requirement is of O(m) locations where m is the size of the matrix A. Numerical examples are given to demonstrate the effectiveness of our efficient solver.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"8 1","pages":"129-138"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74977713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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