ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine最新文献_第6页

Systematic Assessment of RNA-Seq Quantification Tools Using Simulated Sequence Data. 利用模拟序列数据对RNA-Seq定量工具进行系统评估。

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine Pub Date : 2013-09-01 DOI: 10.1145/2506583.2506648

Raghu Chandramohan, Po-Yen Wu, John H Phan, May D Wang

{"title":"Systematic Assessment of RNA-Seq Quantification Tools Using Simulated Sequence Data.","authors":"Raghu Chandramohan, Po-Yen Wu, John H Phan, May D Wang","doi":"10.1145/2506583.2506648","DOIUrl":"10.1145/2506583.2506648","url":null,"abstract":"RNA-sequencing (RNA-seq) technology has emerged as the preferred method for quantification of gene and isoform expression. Numerous RNA-seq quantification tools have been proposed and developed, bringing us closer to developing expression-based diagnostic tests based on this technology. However, because of the rapidly evolving technologies and algorithms, it is essential to establish a systematic method for evaluating the quality of RNA-seq quantification. We investigate how different RNA-seq experimental designs (i.e., variations in sequencing depth and read length) affect various quantification algorithms (i.e., HTSeq, Cufflinks, and MISO). Using simulated data, we evaluate the quantification tools based on four metrics, namely: (1) total number of usable fragments for quantification, (2) detection of genes and isoforms, (3) correlation, and (4) accuracy of expression quantification with respect to the ground truth. Results show that Cufflinks is able to use the largest number of fragments for quantification, leading to better detection of genes and isoforms. However, HTSeq produces more accurate expression estimates. Moreover, each quantification algorithm is affected differently by varying sequencing depth and read length, suggesting that the selection of quantification algorithms should be application-dependent.","PeriodicalId":72044,"journal":{"name":"ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2506583.2506648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34378450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Collective Ranking Method for Genome-wide Association Studies. 全基因组关联研究的集体排序方法。

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine Pub Date : 2012-10-01 DOI: 10.1145/2382936.2382976

Jie Liu, Humberto Vidaillet, Elizabeth Burnside, David Page

{"title":"A Collective Ranking Method for Genome-wide Association Studies.","authors":"Jie Liu, Humberto Vidaillet, Elizabeth Burnside, David Page","doi":"10.1145/2382936.2382976","DOIUrl":"https://doi.org/10.1145/2382936.2382976","url":null,"abstract":"Genome-wide association studies (GWAS) analyze genetic variation (SNPs) across the entire human genome, searching for SNPs that are associated with certain phenotypes, most often diseases, such as breast cancer. In GWAS, we seek a ranking of SNPs in terms of their relevance to the given phenotype. However, because certain SNPs are known to be highly correlated with one another across individuals, it can be beneficial to take into account these correlations when ranking. If a SNP appears associated with the phenotype, and we question whether this association is real, the extent to which its neighbors (correlated SNPs) also appear associated can be informative. Therefore, we propose CollectRank, a ranking approach which allows SNPs to reinforce one another via the correlation structure. CollectRank is loosely analogous to the well-known PageRank algorithm. We first evaluate CollectRank on synthetic data generated from a variety of genetic models under different settings. The numerical results suggest CollectRank can significantly outperform common GWAS methods at the cost of a small amount of extra computation. We further evaluate CollectRank on two real-world GWAS on breast cancer and atrial fibrillation/flutter, and CollectRank performs well in both studies. We finally provide a theoretical analysis that also suggests CollectRank's advantages.","PeriodicalId":72044,"journal":{"name":"ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2382936.2382976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37889997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biological Interpretation of Morphological Patterns in Histopathological Whole-Slide Images. 组织病理学整张切片图像中形态学模式的生物学解读。

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine Pub Date : 2012-10-01 DOI: 10.1145/2382936.2382964

Sonal Kothari, John H Phan, Adeboye O Osunkoya, May D Wang

{"title":"Biological Interpretation of Morphological Patterns in Histopathological Whole-Slide Images.","authors":"Sonal Kothari, John H Phan, Adeboye O Osunkoya, May D Wang","doi":"10.1145/2382936.2382964","DOIUrl":"10.1145/2382936.2382964","url":null,"abstract":"We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.","PeriodicalId":72044,"journal":{"name":"ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5859578/pdf/nihms807306.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35939491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ranking Docked Models of Protein-Protein Complexes Using Predicted Partner-Specific Protein-Protein Interfaces: A Preliminary Study. 利用预测的伴侣特异性蛋白质-蛋白质界面对蛋白质-蛋白质复合物进行排序对接模型:初步研究。

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine Pub Date : 2011-08-01 DOI: 10.1145/2147805.2147866

Li C Xue, Rafael A Jordan, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar

{"title":"Ranking Docked Models of Protein-Protein Complexes Using Predicted Partner-Specific Protein-Protein Interfaces: A Preliminary Study.","authors":"Li C Xue, Rafael A Jordan, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar","doi":"10.1145/2147805.2147866","DOIUrl":"https://doi.org/10.1145/2147805.2147866","url":null,"abstract":"Computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models generated by docking software presents a significant challenge in practice. We introduce a novel method for ranking docked conformations based on the degree of overlap between the interface residues of a docked conformation formed by a pair of proteins with the set of predicted interface residues between them. Our approach relies on a method, called PS-HomPPI, for reliably predicting protein-protein interface residues by taking into account information derived from both interacting proteins. PS-HomPPI infers the residues of a query protein that are likely to interact with a partner protein based on known interface residues of the homo-interologs of the query-partner protein pair, i.e., pairs of interacting proteins that are homologous to the query protein and partner protein. Our results on Docking Benchmark 3.0 show that the quality of the ranking of docked conformations using our method is consistently superior to that produced using ClusPro cluster-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface predictions. An implementation of our method for ranking docked models is freely available at: http://einstein.cs.iastate.edu/DockRank/.","PeriodicalId":72044,"journal":{"name":"ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2147805.2147866","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33243558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Dynamic Visualization and Comparative Analysis of Multiple Collinear Genomic Data. 多个共线性基因组数据的动态可视化与比较分析。

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine Pub Date : 2011-08-01 DOI: 10.1145/2147805.2147846

Jeremy Wang, Fernando Pardo-Manuel de Villena, Leonard McMillan

{"title":"Dynamic Visualization and Comparative Analysis of Multiple Collinear Genomic Data.","authors":"Jeremy Wang, Fernando Pardo-Manuel de Villena, Leonard McMillan","doi":"10.1145/2147805.2147846","DOIUrl":"https://doi.org/10.1145/2147805.2147846","url":null,"abstract":"We have developed a novel tool for visualizing and analyzing multiple collinear genomes. Unlike previous genome browsers and viewers, ours allows for simultaneous and comparative analysis. Our browser is web-based and provides intuitive selection and interactive navigation about features of interest. Dynamic visualizations adjust to scale and data content making analysis at variable resolutions and of multiple data sets more informative. Our tool illustrates genome-sequence similarity through a mosaic of intervals representing local phylogeny, subspecific origin, and haplotype identity. Comparative analysis is facilitated through reordering and clustering of tracks, which can vary throughout the genome. In addition, we provide local phylogenetic trees as an alternate visualization to assess local variations. We demonstrate our genome browser for an extensive set of genomic data sets composed of almost 200 distinct mouse strains.","PeriodicalId":72044,"journal":{"name":"ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2147805.2147846","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35594879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4