生物信息軟件積累

軟件或者程序名稱：GeneCodis來源文獻(xiàn)：GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists說明：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 of 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 publicly available at http:/genecodis.dacya.ucm.es/軟件鏈接：http:/genecodis.dacya.ucm.es/軟件或者程序名稱：OrthoMCL 來源文獻(xiàn)：OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes說明：同時(shí)尋找多個(gè)物種基因組的直系同源基因組Abstract：The identification of orthologous groups is useful for genome annotation, studies 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 sequence 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 genomes, 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 annotations 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 Web interface allows queries based on individual genes or user-defined phylogenetic patterns (/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.軟件或者程序名稱：PA-SUB Server v2.5來源文獻(xiàn)：Predicting subcellular localization of proteins using machine-learned classifiers說明：基因產(chǎn)物（蛋白質(zhì)）的亞細(xì)胞定位。Abstract：Motivation: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. 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產(chǎn)物作用多個(gè)位置). Rather than using sequence information alone, we have explored the use of database text annotations 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, 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.軟件或者程序名稱：TMHMM Web server v2.0來源文獻(xiàn)：1、A hidden Markov model for predicting transmembrane helices in protein sequences2、Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes說明：跨膜蛋白的預(yù)測(cè)。Abstract：A novel method to model and predict the location and orientation(方向) of alpha helices in membrane-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種類型）, loop on the cytoplasmic side, two loops for 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 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 single sequences, our transmembrane HMM, TMHMM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 83 proteins with known topology. The same accuracy was achieved on a larger dataset of 160 proteins. These results compare favourably with existing methods.Abstract：We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMMs performance, and show that it correctly predicts 9798 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 2030 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with Nin-Cin topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for Nout-Cin topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http:/www.cbs.dtu.dk/services/TMHMM/.軟件或者程序名稱：tYNA來源文獻(xiàn)：The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks說明：蛋白互作網(wǎng)絡(luò)統(tǒng)計(jì)分析Abstract：Summary: Biological processes involve complex networks of interactions between molecules. Various large-scale experiments and curation efforts have led to preliminary versions of complete cellular networks for a number of organisms. To grapple with these networks, we developed TopNet-like Yale Network Analyzer (tYNA), a Web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA efficiently implements methods that have proven useful in network analysis, including identifying defective cliques, finding small network motifs (such as feed-forward loops), calculating global statistics (such as the clustering coefficient and eccentricity), and identifying hubs and bottlenecks. It also allows one to manage a large number of private and public networks using a flexible tagging system, to filter them based on a variety of criteria, and to visualize them through an interactive graphical interface. A number of commonly used biological datasets have been pre-loaded into tYNA, standardized and grouped into different categories. Availability: The tYNA system can be accessed at /tyna. The source code, JavaDoc API and WSDL can also be downloaded from the website. tYNA can also be accessed from the Cytoscape software using a plugin.軟件鏈接：/tyna/軟件或者程序名稱：enoLOGOS來源文獻(xiàn)：enoLOGOS: a versatile web tool for energy normalized sequence logos說明：用于圖形顯示短序列的各個(gè)位點(diǎn)的保守信息Abstract：enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned se