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Tayyaba Yasmin Inayat Ur Rehman Adnan Ahmad Ansari Khurrum liaqat Muhammad Irfan khan 《Bioinformation》2012,8(25):1277-1279
The availability of genomic sequences of many organisms has opened new challenges in many aspects particularly in terms of
genome analysis. Sequence extraction is a vital step and many tools have been developed to solve this issue. These tools are
available publically but have limitations with reference to the sequence extraction, length of the sequence to be extracted, organism
specificity and lack of user friendly interface. We have developed a java based software package having three modules which can
be used independently or sequentially. The tool efficiently extracts sequences from large datasets with few simple steps. It can
efficiently extract multiple sequences of any desired length from a genome of any organism. The results are crosschecked by
published data.
Availability
URL 1: http://ww3.comsats.edu.pk/bio/ResearchProjects.aspxURL 2: http://ww3.comsats.edu.pk/bio/SequenceManeuverer.aspx 相似文献2.
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Basic Local Alignment Search Tool, (BLAST) allows the comparison of a query sequence/s
to a database of sequences and identifies those sequences that are similar to the query above a
user-defined threshold. We have developed a user friendly web application, MULTBLAST that runs a
series of BLAST searches on a user-supplied list of proteins against one or more target protein or
nucleotide databases. The application pre-processes the data, launches each individual BLAST search
on the University of Nevada, Reno''s-TimeLogic DeCypher® system (available from
Active Motif, Inc.) and retrieves and combines all the results into a simple, easy to read output file.
The output file presents the list of the query proteins, followed by the BLAST results for the matching
sequences from each target database in consecutive columns. This format is especially useful for
either comparing the results from the different target databases, or analyzing the results while keeping
the identification of each target database separate.
Availability
The application is available at the URLhttp://blastpipe.biochem.unr.edu/ 相似文献6.
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Sean R Landman Tae Hyun Hwang Kevin AT Silverstein Yingming Li Scott M Dehm Michael Steinbach Vipin Kumar 《BMC genomics》2014,15(1)
Background
Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis.Results
By making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications.Conclusion
SHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-84) contains supplementary material, which is available to authorized users. 相似文献12.
Motivation
The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences.Methods
In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences.Results
Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction.Availability
The web server and datasets are freely available at http://bcell.whu.edu.cn. 相似文献13.
Background
Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.Results
In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.Conclusions
The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users. 相似文献14.
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Background
The molecular network sustained by different types of interactions among proteins is widely manifested as the fundamental driving force of cellular operations. Many biological functions are determined by the crosstalk between proteins rather than by the characteristics of their individual components. Thus, the searches for protein partners in global networks are imperative when attempting to address the principles of biology.Results
We have developed a web-based tool “Sequence-based Protein Partners Search” (SPPS) to explore interacting partners of proteins, by searching over a large repertoire of proteins across many species. SPPS provides a database containing more than 60,000 protein sequences with annotations and a protein-partner search engine in two modes (Single Query and Multiple Query). Two interacting proteins of human FBXO6 protein have been found using the service in the study. In addition, users can refine potential protein partner hits by using annotations and possible interactive network in the SPPS web server.Conclusions
SPPS provides a new type of tool to facilitate the identification of direct or indirect protein partners which may guide scientists on the investigation of new signaling pathways. The SPPS server is available to the public at http://mdl.shsmu.edu.cn/SPPS/. 相似文献17.
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GSTaxClassifier (Genomic Signature based Taxonomic Classifier) is a program for metagenomics analysis of shotgun DNA sequences. The
program includes
- a simple but effective algorithm, a modification of the Bayesian method, to predict the most probable genomic origins of sequences at different taxonomical ranks, on the basis of genome databases;
- a function to generate genomic profiles of reference sequences with tri-, tetra-, penta-, and hexa-nucleotide motifs for setting a user-defined database;
- two different formats (tabular- and tree-based summaries) to display taxonomic predictions with improved analytical methods; and
- effective ways to retrieve, search, and summarize results by integrating the predictions into the NCBI tree-based taxonomic information.
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Background
Recognizing regulatory sequences in genomes is a continuing challenge, despite a wealth of available genomic data and a growing number of experimentally validated examples.Methodology/Principal Findings
We discuss here a simple approach to search for regulatory sequences based on the compositional similarity of genomic regions and known cis-regulatory sequences. This method, which is not limited to searching for predefined motifs, recovers sequences known to be under similar regulatory control. The words shared by the recovered sequences often correspond to known binding sites. Furthermore, we show that although local word profile clustering is predictive for the regulatory sequences involved in blastoderm segmentation, local dissimilarity is a more universal feature of known regulatory sequences in Drosophila.Conclusions/Significance
Our method leverages sequence motifs within a known regulatory sequence to identify co-regulated sequences without explicitly defining binding sites. We also show that regulatory sequences can be distinguished from surrounding sequences by local sequence dissimilarity, a novel feature in identifying regulatory sequences across a genome. Source code for WPH-finder is available for download at http://rana.lbl.gov/downloads/wph.tar.gz. 相似文献20.
Mohit Midha Raja Polavarapu Potshangbam Angamba Meetei Hari Krishnan Krishnaveni Mohareer Vaibhav Vindal 《Bioinformation》2012,8(15):738-739
Identification of ortholog is one of the important tasks to understand a novel genome. It helps to assign functional annotations,
from one organism to another organism. To identify the putative ortholog, Reciprocal Best BLAST hit (RBBH) method is known to
be an efficient approach. OrFin makes use of the same approach to identify pair of orthologous proteins for a given set of sequences
of two species. It is a user-friendly web tool which works with user defined parameters to search RBBHs. Results are produced in
both html and text format.