Proceedings. International Conference on Intelligent Systems for Molecular Biology最新文献

Reducing Mass Degeneracy in SAR by MS by Stable Isotopic Labeling 稳定同位素标记的质谱法还原SAR中的质量简并

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-08-19 DOI: 10.1089/106652701300099056

C. Bailey-Kellogg, J. Kelley, Clifford Stein, B. Donald

{"title":"Reducing Mass Degeneracy in SAR by MS by Stable Isotopic Labeling","authors":"C. Bailey-Kellogg, J. Kelley, Clifford Stein, B. Donald","doi":"10.1089/106652701300099056","DOIUrl":"https://doi.org/10.1089/106652701300099056","url":null,"abstract":"Mass spectrometry (MS) promises to be an invaluable tool for functional genomics, by supporting low-cost, high-throughput experiments. However, large-scale MS faces the potential problem of mass degeneracy---indistinguishable masses for multiple biopolymer fragments (e.g., from a limited proteolytic digest). This paper studies the tasks of planning and interpreting MS experiments that use selective isotopic labeling, thereby substantially reducing potential mass degeneracy. Our algorithms support an experimental--computational protocol called structure-activity relation by mass spectrometry (SAR by MS) for elucidating the function of protein-DNA and protein-protein complexes. SAR by MS enzymatically cleaves a crosslinked complex and analyzes the resulting mass spectrum for mass peaks of hypothesized fragments. Depending on binding mode, some cleavage sites will be shielded; the absence of anticipated peaks implicates corresponding fragments as either part of the interaction region or inaccessible due to conformational change upon binding. Thus, different mass spectra provide evidence for different structure--activity relations. We address combinatorial and algorithmic questions in the areas of data analysis (constraining binding mode based on mass signature) and experiment planning (determining an isotopic labeling strategy to reduce mass degeneracy and aid data analysis). We explore the computational complexity of these problems, obtaining upper and lower bounds. We report experimental results from implementations of our algorithms.","PeriodicalId":79420,"journal":{"name":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2000-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1089/106652701300099056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60598839","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}

引用次数: 3

Protein family classification using sparse Markov transducers. 利用稀疏马尔可夫传感器进行蛋白质家族分类。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

E Eskin, W N Grundy, Y Singer

引用次数: 0

A practical algorithm for optimal inference of haplotypes from diploid populations. 从二倍体群体中最优推断单倍型的实用算法。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

D Gusfield

{"title":"A practical algorithm for optimal inference of haplotypes from diploid populations.","authors":"D Gusfield","doi":"","DOIUrl":"","url":null,"abstract":"The next phase of human genomics will involve large-scale screens of populations for significant DNA polymorphisms, notably single nucleotide polymorphisms (SNP's). Dense human SNP maps are currently under construction. However, the utility of those maps and screens will be limited by the fact that humans are diploid, and that it is presently difficult to get separate data on the two \"copies\". Hence genotype (blended) SNP data will be collected, and the desired haplotype (partitioned) data must then be (partially) inferred. A particular non-deterministic inference algorithm was proposed and studied before SNP data was available, and extensively applied more recently to study the first available SNP data. In this paper, we consider the question of whether we can obtain an efficient, deterministic variant of that method to optimize the obtained inferences. Although we have shown elsewhere that the optimization problem is NP-hard, we present here a practical approach based on (integer) linear programming. The method either returns the optimal answer, and a declaration that it is the optimal, or declares that it has failed to find the optimal. The approach works quickly and correctly, finding the optimal on all simulated data tested, data that is expected to be more demanding than realistic biological data.","PeriodicalId":79420,"journal":{"name":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21811344","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}

引用次数: 0

Analysis of yeast's ORF upstream regions by parallel processing, microarrays, and computational methods. 利用并行处理、微阵列和计算方法分析酵母ORF上游区域。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

S Hampson, P Baldi, D Kibler, S B Sandmeyer

引用次数: 0

A pragmatic information extraction strategy for gathering data on genetic interactions. 一种实用的基因相互作用信息提取策略。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

D Proux, F Rechenmann, L Julliard

引用次数: 0

Sequence database search using jumping alignments. 序列数据库搜索使用跳跃对齐。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

R Spang, M Rehmsmeier, J Stoye

{"title":"Sequence database search using jumping alignments.","authors":"R Spang, M Rehmsmeier, J Stoye","doi":"","DOIUrl":"","url":null,"abstract":"We describe a new algorithm for amino acid sequence classification and the detection of remote homologues. The rationale is to exploit both vertical and horizontal information of a multiple alignment in a well balanced manner. This is in contrast to established methods like profiles and hidden Markov models which focus on vertical information as they model the columns of the alignment independently. In our setting, we want to select from a given database of \"candidate sequences\" those proteins that belong to a given superfamily. In order to do so, each candidate sequence is separately tested against a multiple alignment of the known members of the superfamily by means of a new jumping alignment algorithm. This algorithm is an extension of the Smith-Waterman algorithm and computes a local alignment of a single sequence and a multiple alignment. In contrast to traditional methods, however, this alignment is not based on a summary of the individual columns of the multiple alignment. Rather, the candidate sequence at each position is aligned to one sequence of the multiple alignment, called the \"reference sequence\". In addition, the reference sequence may change within the alignment, while each such jump is penalized. To evaluate the discriminative quality of the jumping alignment algorithm, we compared it to hidden Markov models on a subset of the SCOP database of protein domains. The discriminative quality was assessed by counting the number of false positives that ranked higher than the first true positive (FP-count). For moderate FP-counts above five, the number of successful searches with our method was considerably higher than with hidden Markov models.","PeriodicalId":79420,"journal":{"name":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"21813096","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}

引用次数: 0

Finding regulatory elements using joint likelihoods for sequence and expression profile data. 利用序列和表达谱数据的联合似然来寻找调控元件。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

I Holmes, W J Bruno

引用次数: 0

The conserved exon method for gene finding. 保守外显子法寻找基因。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

V Bafna, D H Huson

引用次数: 0

A probabilistic learning approach to whole-genome operon prediction. 全基因组操纵子预测的概率学习方法。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

M Craven, D Page, J Shavlik, J Bockhorst, J Glasner

引用次数: 0

Regulatory element detection using a probabilistic segmentation model. 基于概率分割模型的调控元素检测。

Proceedings. International Conference on Intelligent Systems for Molecular Biology Pub Date : 2000-01-01

H J Bussemaker, H Li, E D Siggia

引用次数: 0