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  • 1
    Online Resource
    Online Resource
    Variation block-based genomics method for crop plants
    Springer Science and Business Media LLC ; 2014
    In:  BMC Genomics Vol. 15, No. 1 ( 2014), p. 477-
    Details
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 15, No. 1 ( 2014), p. 477-
    Type of Medium: Online Resource
    ISSN: 1471-2164
    URL: Article
    DOI: 10.1186/1471-2164-15-477
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
    detail.hit.zdb_id: 2041499-7
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  • 2
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    Comparison and evaluation of pathway-level aggregation methods of gene expression data
    Springer Science and Business Media LLC ; 2012
    In:  BMC Genomics Vol. 13, No. S7 ( 2012-12)
    Details
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 13, No. S7 ( 2012-12)
    Type of Medium: Online Resource
    ISSN: 1471-2164
    URL: Article
    DOI: 10.1186/1471-2164-13-S7-S26
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2012
    detail.hit.zdb_id: 2041499-7
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  • 3
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    Whole genome sequencing of an ethnic Pathan (Pakhtun) from the north-west of Pakistan
    Springer Science and Business Media LLC ; 2015
    In:  BMC Genomics Vol. 16, No. 1 ( 2015-12)
    Details
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 16, No. 1 ( 2015-12)
    Type of Medium: Online Resource
    ISSN: 1471-2164
    URL: Article
    DOI: 10.1186/s12864-015-1290-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2015
    detail.hit.zdb_id: 2041499-7
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  • 4
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    Using evolutionary and structural information to predict DNA‐binding sites on DNA‐binding proteins
    Wiley ; 2006
    In:  Proteins: Structure, Function, and Bioinformatics Vol. 64, No. 1 ( 2006-07), p. 19-27
    Details
    In: Proteins: Structure, Function, and Bioinformatics, Wiley, Vol. 64, No. 1 ( 2006-07), p. 19-27
    Abstract: Proteins that interact with DNA are involved in a number of fundamental biological activities such as DNA replication, transcription, and repair. A reliable identification of DNA‐binding sites in DNA‐binding proteins is important for functional annotation, site‐directed mutagenesis, and modeling protein–DNA interactions. We apply Support Vector Machine (SVM), a supervised pattern recognition method, to predict DNA‐binding sites in DNA‐binding proteins using the following features: amino acid sequence, profile of evolutionary conservation of sequence positions, and low‐resolution structural information. We use a rigorous statistical approach to study the performance of predictors that utilize different combinations of features and how this performance is affected by structural and sequence properties of proteins. Our results indicate that an SVM predictor based on a properly scaled profile of evolutionary conservation in the form of a position specific scoring matrix (PSSM) significantly outperforms a PSSM‐based neural network predictor. The highest accuracy is achieved by SVM predictor that combines the profile of evolutionary conservation with low‐resolution structural information. Our results also show that knowledge‐based predictors of DNA‐binding sites perform significantly better on proteins from mainly‐α structural class and that the performance of these predictors is significantly correlated with certain structural and sequence properties of proteins. These observations suggest that it may be possible to assign a reliability index to the overall accuracy of the prediction of DNA‐binding sites in any given protein using its sequence and structural properties. A web‐server implementation of the predictors is freely available online at http://lcg.rit.albany.edu/dp‐bind/ . Proteins 2006. © 2006 Wiley‐Liss, Inc.
    Type of Medium: Online Resource
    ISSN: 0887-3585 , 1097-0134
    URL: Issue
    URL: Article
    DOI: 10.1002/prot.v64:1
    DOI: 10.1002/prot.20977
    RVK:
    WA 15000
    Language: English
    Publisher: Wiley
    Publication Date: 2006
    detail.hit.zdb_id: 1475032-6
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  • 5
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    Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq
    Springer Science and Business Media LLC ; 2012
    In:  BMC Genomics Vol. 13, No. 1 ( 2012-12)
    Details
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2012-12)
    Abstract: Thoroughbred horses are the most expensive domestic animals, and their running ability and knowledge about their muscle-related diseases are important in animal genetics. While the horse reference genome is available, there has been no large-scale functional annotation of the genome using expressed genes derived from transcriptomes. Results We present a large-scale analysis of whole transcriptome data. We sequenced the whole mRNA from the blood and muscle tissues of six thoroughbred horses before and after exercise. By comparing current genome annotations, we identified 32,361 unigene clusters spanning 51.83 Mb that contained 11,933 (36.87%) annotated genes. More than 60% (20,428) of the unigene clusters did not match any current equine gene model. We also identified 189,973 single nucleotide variations (SNVs) from the sequences aligned against the horse reference genome. Most SNVs (171,558 SNVs; 90.31%) were novel when compared with over 1.1 million equine SNPs from two SNP databases. Using differential expression analysis, we further identified a number of exercise-regulated genes: 62 up-regulated and 80 down-regulated genes in the blood, and 878 up-regulated and 285 down-regulated genes in the muscle. Six of 28 previously-known exercise-related genes were over-expressed in the muscle after exercise. Among the differentially expressed genes, there were 91 transcription factor-encoding genes, which included 56 functionally unknown transcription factor candidates that are probably associated with an early regulatory exercise mechanism. In addition, we found interesting RNA expression patterns where different alternative splicing forms of the same gene showed reversed expressions before and after exercising. Conclusion The first sequencing-based horse transcriptome data, extensive analyses results, deferentially expressed genes before and after exercise, and candidate genes that are related to the exercise are provided in this study.
    Type of Medium: Online Resource
    ISSN: 1471-2164
    URL: Article
    DOI: 10.1186/1471-2164-13-473
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2012
    detail.hit.zdb_id: 2041499-7
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  • 6
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    A novel sensitive method for the detection of user-defined compositional bias in biological sequences
    Oxford University Press (OUP) ; 2006
    In:  Bioinformatics Vol. 22, No. 9 ( 2006-05-01), p. 1055-1063
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 9 ( 2006-05-01), p. 1055-1063
    Abstract: Motivation: Most biological sequences contain compositionally biased segments in which one or more residue types are significantly overrepresented. The function and evolution of these segments are poorly understood. Usually, all types of compositionally biased segments are masked and ignored during sequence analysis. However, it has been shown for a number of proteins that biased segments that contain amino acids with similar chemical properties are involved in a variety of molecular functions and human diseases. A detailed large-scale analysis of the functional implications and evolutionary conservation of different compositionally biased segments requires a sensitive method capable of detecting user-specified types of compositional bias. Results: We present BIAS, a novel sensitive method for the detection of compositionally biased segments composed of a user-specified set of residue types. BIAS uses the discrete scan statistics that provides a highly accurate correction for multiple tests to compute analytical estimates of the significance of each compositionally biased segment. The method can take into account global compositional bias when computing analytical estimates of the significance of local clusters. BIAS is benchmarked against SEG, SAPS and CAST programs. We also use BIAS to show that groups of proteins with the same biological function are significantly associated with particular types of compositionally biased segments. Availability: The software is available at Contact:  ikuznetsov@albany.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btl049
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2006
    detail.hit.zdb_id: 1468345-3
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  • 7
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    DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins
    Oxford University Press (OUP) ; 2007
    In:  Bioinformatics Vol. 23, No. 5 ( 2007-03-01), p. 634-636
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 5 ( 2007-03-01), p. 634-636
    Abstract: Summary: This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence. Availability: Freely available at http://lcg.rit.albany.edu/dp-bind Contact:  IKuznetsov@albany.edu Supplementry information:  http://lcg.rit.albany.edu/dp-bind/dpbind_supplement.html
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btl672
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2007
    detail.hit.zdb_id: 1468345-3
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  • 8
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    Liverome: a curated database of liver cancer-related gene signatures with self-contained context information
    Springer Science and Business Media LLC ; 2011
    In:  BMC Genomics Vol. 12, No. S3 ( 2011-12)
    Details
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 12, No. S3 ( 2011-12)
    Abstract: Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. A number of molecular profiling studies have investigated the changes in gene and protein expression that are associated with various clinicopathological characteristics of HCC and generated a wealth of scattered information, usually in the form of gene signature tables. A database of the published HCC gene signatures would be useful to liver cancer researchers seeking to retrieve existing differential expression information on a candidate gene and to make comparisons between signatures for prioritization of common genes. A challenge in constructing such database is that a direct import of the signatures as appeared in articles would lead to a loss or ambiguity of their context information that is essential for a correct biological interpretation of a gene’s expression change. This challenge arises because designation of compared sample groups is most often abbreviated, ad hoc , or even missing from published signature tables. Without manual curation, the context information becomes lost, leading to uninformative database contents. Although several databases of gene signatures are available, none of them contains informative form of signatures nor shows comprehensive coverage on liver cancer. Thus we constructed Liverome, a curated database of liver cancer-related gene signatures with self-contained context information. Description Liverome’s data coverage is more than three times larger than any other signature database, consisting of 143 signatures taken from 98 HCC studies, mostly microarray and proteome, and involving 6,927 genes. The signatures were post-processed into an informative and uniform representation and annotated with an itemized summary so that all context information is unambiguously self-contained within the database. The signatures were further informatively named and meaningfully organized according to ten functional categories for guided browsing. Its web interface enables a straightforward retrieval of known differential expression information on a query gene and a comparison of signatures to prioritize common genes. The utility of Liverome-collected data is shown by case studies in which useful biological insights on HCC are produced. Conclusion Liverome database provides a comprehensive collection of well-curated HCC gene signatures and straightforward interfaces for gene search and signature comparison as well. Liverome is available at http://liverome.kobic.re.kr .
    Type of Medium: Online Resource
    ISSN: 1471-2164
    URL: Article
    DOI: 10.1186/1471-2164-12-S3-S3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2011
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  • 9
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    Genome-wide identification of palmitate-regulated immediate early genes and target genes in pancreatic beta-cells reveals a central role of NF-κB
    Springer Science and Business Media LLC ; 2012
    In:  Molecular Biology Reports Vol. 39, No. 6 ( 2012-6), p. 6781-6789
    Details
    In: Molecular Biology Reports, Springer Science and Business Media LLC, Vol. 39, No. 6 ( 2012-6), p. 6781-6789
    Type of Medium: Online Resource
    ISSN: 0301-4851 , 1573-4978
    URL: Article
    DOI: 10.1007/s11033-012-1503-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2012
    detail.hit.zdb_id: 1478217-0
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