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1、软件或者程序名称：软件或者程序名称：GeneCodis 来源文献：来源文献：GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists 说明：说明：GO 富含分析 Abstract： We present GENECODIS, a web-based tool that integrates different sources of information to search for annotations that frequently co-occur in a set o
2、f genes and rank them by statistical significance. The analysis of concurrent annotations provides significant information for the biologic interpretation of high-throughput experiments and may outperform the results of standard methods for the functional analysis of gene lists. GENECODIS is publicl
3、y available at http:/genecodis.dacya.ucm.es/ 软件链接：软件链接：http:/genecodis.dacya.ucm.es/软件或者程序名称：软件或者程序名称：OrthoMCL 来源文献：来源文献：OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes 说明：说明：同时寻找多个物种基因组的直系同源基因组 Abstract： The identification of orthologous groups is useful for genome annotation, st
4、udies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequ
5、ence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two g
6、enomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of “recent” paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotatio
7、ns suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A
8、Web interface allows queries based on individual genes or user-defined phylogenetic patterns (http:/www.cbil.upenn.edu/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.软件或
9、者程序名称：软件或者程序名称：PA-SUB Server v2.5 来源文献：来源文献：Predicting subcellular localization of proteins using machine-learned classifiers 说明：说明：基因产物（蛋白质）的亚细胞定位。 Abstract： Motivation: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purificatio
10、n. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage(我 猜想指的是可能基因产物作用多个位置). Rather than using sequence information alone, we have explored the use of database text annot
11、ations from homologs and machine learning to substantially improve the prediction of subcellular location. Results: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria
12、, which are 81% accurate for fungi and 9294% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service.软件或者程序名称：软件或者程序名称：TMHMM Web server v2.0 来源文献：来源文献： 1、
13、A hidden Markov model for predicting transmembrane helices in protein sequences 2、Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes 说明：说明：跨膜蛋白的预测。 Abstract： A novel method to model and predict the location and orientation(方向) of alpha helices in me
14、mbrane-spanning proteins(跨膜蛋白) is presented. It is based on a hidden Markov model (HMM) with an architecture that corresponds closely to the biological system. The model is cyclic with 7 types of states for helix core, helix caps on either side（包括 2 种类型）, loop on the cytoplasmic side, two loops for
15、the non-cytoplasmic side, and a globular domain state in the middle of each loop. The two loop paths on the non-cytoplasmic side are used to model short and long loops separately, which corresponds biologically to the two known different membrane insertions mechanisms. The close mapping between the
16、biological and computational states allows us to infer which parts of the model architecture are important to capture the information that encodes the membrane topology, and to gain a better understanding of the mechanisms and constraints involved. Models were estimated both by maximum likelihood and a discriminative method, and a method for reassignment of the membrane helix boundaries were developed. In a cross validated test on sin
