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Showing 25 to 36 of 109 entries
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GFF Utilities: GffRead and GffCompare.

F1000Research

Pertea G, Pertea M.
PMID: 32489650
F1000Res. 2020 Apr 28;9:304. doi: 10.12688/f1000research.23297.1. eCollection 2020.

No abstract available.

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Pertea G, Pertea M. GFF Utilities: GffRead and GffCompare. F1000Res. 2020;9:304doi: 10.12688/f1000research.23297.1.
Pertea, G., & Pertea, M. (2020). GFF Utilities: GffRead and GffCompare. F1000Research, 9304. https://doi.org/10.12688/f1000research.23297.1
Pertea, Geo, and Pertea, Mihaela. "GFF Utilities: GffRead and GffCompare." F1000Research vol. 9 (2020): 304. doi: https://doi.org/10.12688/f1000research.23297.1
Pertea G, Pertea M. GFF Utilities: GffRead and GffCompare. F1000Res. 2020 Apr 28;9:304. doi: 10.12688/f1000research.23297.1. eCollection 2020. PMID: 32489650; PMCID: PMC7222033.
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MapMan Visualization of RNA-Seq Data Using Mercator4 Functional Annotations.

Methods in molecular biology (Clifton, N.J.)

Bolger M, Schwacke R, Usadel B.
PMID: 34448161
Methods Mol Biol. 2021;2354:195-212. doi: 10.1007/978-1-0716-1609-3_9.

Plant omics research has advanced to the stage where it is feasible to generate data from multiple samples and multiple time points to gain insight into biological processes. This impressive array of data can prove challenging to interpret. In...

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Bolger M, Schwacke R, Usadel B. MapMan Visualization of RNA-Seq Data Using Mercator4 Functional Annotations. Methods Mol Biol. 2021;2354:195-212doi: 10.1007/978-1-0716-1609-3_9.
Bolger, M., Schwacke, R., & Usadel, B. (2021). MapMan Visualization of RNA-Seq Data Using Mercator4 Functional Annotations. Methods in molecular biology (Clifton, N.J.), 2354195-212. https://doi.org/10.1007/978-1-0716-1609-3_9
Bolger, Marie, et al. "MapMan Visualization of RNA-Seq Data Using Mercator4 Functional Annotations." Methods in molecular biology (Clifton, N.J.) vol. 2354 (2021): 195-212. doi: https://doi.org/10.1007/978-1-0716-1609-3_9
Bolger M, Schwacke R, Usadel B. MapMan Visualization of RNA-Seq Data Using Mercator4 Functional Annotations. Methods Mol Biol. 2021;2354:195-212. doi: 10.1007/978-1-0716-1609-3_9. PMID: 34448161.
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A computational interactome and functional annotation for the human proteome.

eLife

Garzón JI, Deng L, Murray D, Shapira S, Petrey D, Honig B.
PMID: 27770567
Elife. 2016 Oct 22;5. doi: 10.7554/eLife.18715.

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The...

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Garzón JI, Deng L, Murray D, et al. A computational interactome and functional annotation for the human proteome. Elife. 2016;5doi: 10.7554/eLife.18715.
Garzón, J. I., Deng, L., Murray, D., Shapira, S., Petrey, D., & Honig, B. (2016). A computational interactome and functional annotation for the human proteome. eLife, 5. https://doi.org/10.7554/eLife.18715
Garzón, José Ignacio, et al. "A computational interactome and functional annotation for the human proteome." eLife vol. 5 (2016). doi: https://doi.org/10.7554/eLife.18715
Garzón JI, Deng L, Murray D, Shapira S, Petrey D, Honig B. A computational interactome and functional annotation for the human proteome. Elife. 2016 Oct 22;5. doi: 10.7554/eLife.18715. PMID: 27770567; PMCID: PMC5115866.
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Integrating proteomics profiling data sets: a network perspective.

Methods in molecular biology (Clifton, N.J.)

Bhat A, Dakna M, Mischak H.
PMID: 25384750
Methods Mol Biol. 2015;1243:237-53. doi: 10.1007/978-1-4939-1872-0_14.

Understanding disease mechanisms often requires complex and accurate integration of cellular pathways and molecular networks. Systems biology offers the possibility to provide a comprehensive map of the cell's intricate wiring network, which can ultimately lead to decipher the disease...

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Bhat A, Dakna M, Mischak H. Integrating proteomics profiling data sets: a network perspective. Methods Mol Biol. 2015;1243:237-53doi: 10.1007/978-1-4939-1872-0_14.
Bhat, A., Dakna, M., & Mischak, H. (2015). Integrating proteomics profiling data sets: a network perspective. Methods in molecular biology (Clifton, N.J.), 1243237-53. https://doi.org/10.1007/978-1-4939-1872-0_14
Bhat, Akshay, et al. "Integrating proteomics profiling data sets: a network perspective." Methods in molecular biology (Clifton, N.J.) vol. 1243 (2015): 237-53. doi: https://doi.org/10.1007/978-1-4939-1872-0_14
Bhat A, Dakna M, Mischak H. Integrating proteomics profiling data sets: a network perspective. Methods Mol Biol. 2015;1243:237-53. doi: 10.1007/978-1-4939-1872-0_14. PMID: 25384750.
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Data Warehousing with TargetMine for Omics Data Analysis.

Methods in molecular biology (Clifton, N.J.)

Chen YA, Tripathi LP, Mizuguchi K.
PMID: 31115884
Methods Mol Biol. 2019;1986:35-64. doi: 10.1007/978-1-4939-9442-7_3.

Most biological processes including diseases are multifactorial and determined by a complex interplay of various genetic and environmental factors. This chapter aims to provide a user guide to data querying, analysis, and visualization with TargetMine and the associated auxiliary...

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Chen YA, Tripathi LP, Mizuguchi K. Data Warehousing with TargetMine for Omics Data Analysis. Methods Mol Biol. 2019;1986:35-64doi: 10.1007/978-1-4939-9442-7_3.
Chen, Y. A., Tripathi, L. P., & Mizuguchi, K. (2019). Data Warehousing with TargetMine for Omics Data Analysis. Methods in molecular biology (Clifton, N.J.), 198635-64. https://doi.org/10.1007/978-1-4939-9442-7_3
Chen, Yi-An, et al. "Data Warehousing with TargetMine for Omics Data Analysis." Methods in molecular biology (Clifton, N.J.) vol. 1986 (2019): 35-64. doi: https://doi.org/10.1007/978-1-4939-9442-7_3
Chen YA, Tripathi LP, Mizuguchi K. Data Warehousing with TargetMine for Omics Data Analysis. Methods Mol Biol. 2019;1986:35-64. doi: 10.1007/978-1-4939-9442-7_3. PMID: 31115884.
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CasLocusAnno: a web-based server for annotating cas loci and their corresponding (sub)types.

FEBS letters

Dong C, Zeng Z, Pu DK, Wen QF, Liu S, Du MZ, Sun Y, Gao YZ, Rao N, Huang J, Guo FB.
PMID: 31260103
FEBS Lett. 2019 Sep;593(18):2646-2654. doi: 10.1002/1873-3468.13519. Epub 2019 Jul 17.

In prokaryotes, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein (Cas) systems constitute adaptive immune systems against mobile genetic elements (MGEs). Here, we introduce the Markov cluster algorithm (MCL) to Makarova et al.'s method in order to...

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Dong C, Zeng Z, Pu DK, et al. CasLocusAnno: a web-based server for annotating cas loci and their corresponding (sub)types. FEBS Lett. 2019;593(18):2646-2654doi: 10.1002/1873-3468.13519.
Dong, C., Zeng, Z., Pu, D. K., Wen, Q. F., Liu, S., Du, M. Z., Sun, Y., Gao, Y. Z., Rao, N., Huang, J., & Guo, F. B. (2019). CasLocusAnno: a web-based server for annotating cas loci and their corresponding (sub)types. FEBS letters, 593(18), 2646-2654. https://doi.org/10.1002/1873-3468.13519
Dong, Chuan, et al. "CasLocusAnno: a web-based server for annotating cas loci and their corresponding (sub)types." FEBS letters vol. 593,18 (2019): 2646-2654. doi: https://doi.org/10.1002/1873-3468.13519
Dong C, Zeng Z, Pu DK, Wen QF, Liu S, Du MZ, Sun Y, Gao YZ, Rao N, Huang J, Guo FB. CasLocusAnno: a web-based server for annotating cas loci and their corresponding (sub)types. FEBS Lett. 2019 Sep;593(18):2646-2654. doi: 10.1002/1873-3468.13519. Epub 2019 Jul 17. PMID: 31260103.
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An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies.

Briefings in bioinformatics

Li B, Lu Q, Zhao H.
PMID: 29106447
Brief Bioinform. 2019 May 21;20(3):995-1003. doi: 10.1093/bib/bbx131.

Functionally annotating genetic variations is an essential yet challenging topic in human genetics research. As large consortia including ENCODE and Roadmap Epigenomics Project continue to generate high-throughput transcriptomic and epigenomic data, many computational frameworks have been developed to integrate...

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Li B, Lu Q, Zhao H. An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies. Brief Bioinform. 2019;20(3):995-1003doi: 10.1093/bib/bbx131.
Li, B., Lu, Q., & Zhao, H. (2019). An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies. Briefings in bioinformatics, 20(3), 995-1003. https://doi.org/10.1093/bib/bbx131
Li, Boyang, et al. "An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies." Briefings in bioinformatics vol. 20,3 (2019): 995-1003. doi: https://doi.org/10.1093/bib/bbx131
Li B, Lu Q, Zhao H. An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies. Brief Bioinform. 2019 May 21;20(3):995-1003. doi: 10.1093/bib/bbx131. PMID: 29106447; PMCID: PMC6585153.
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Text-mining-assisted biocuration workflows in Argo.

Database : the journal of biological databases and curation

Rak R, Batista-Navarro RT, Rowley A, Carter J, Ananiadou S.
PMID: 25037308
Database (Oxford). 2014 Jul 18;2014. doi: 10.1093/database/bau070. Print 2014.

Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the...

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Rak R, Batista-Navarro RT, Rowley A, et al. Text-mining-assisted biocuration workflows in Argo. Database (Oxford). 2014;2014doi: 10.1093/database/bau070.
Rak, R., Batista-Navarro, R. T., Rowley, A., Carter, J., & Ananiadou, S. (2014). Text-mining-assisted biocuration workflows in Argo. Database : the journal of biological databases and curation, 2014. https://doi.org/10.1093/database/bau070
Rak, Rafal, et al. "Text-mining-assisted biocuration workflows in Argo." Database : the journal of biological databases and curation vol. 2014 (2014). doi: https://doi.org/10.1093/database/bau070
Rak R, Batista-Navarro RT, Rowley A, Carter J, Ananiadou S. Text-mining-assisted biocuration workflows in Argo. Database (Oxford). 2014 Jul 18;2014. doi: 10.1093/database/bau070. Print 2014. PMID: 25037308; PMCID: PMC4103424.
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Update on activities at the Universal Protein Resource (UniProt) in 2013.

Nucleic acids research

[No authors listed]
PMID: 23161681
Nucleic Acids Res. 2013 Jan;41:D43-7. doi: 10.1093/nar/gks1068. Epub 2012 Nov 17.

The mission of the Universal Protein Resource (UniProt) (http://www.uniprot.org) is to support biological research by providing a freely accessible, stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase. It integrates, interprets and standardizes data from numerous resources...

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Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. 2012;41:D43-7doi: 10.1093/nar/gks1068.
(2013). Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic acids research, 41D43-7. https://doi.org/10.1093/nar/gks1068
"Update on activities at the Universal Protein Resource (UniProt) in 2013." Nucleic acids research vol. 41 (2013): D43-7. doi: https://doi.org/10.1093/nar/gks1068
Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. 2013 Jan;41:D43-7. doi: 10.1093/nar/gks1068. Epub 2012 Nov 17. PMID: 23161681; PMCID: PMC3531094.
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Molecular signatures database (MSigDB) 3.0.

Bioinformatics (Oxford, England)

Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP.
PMID: 21546393
Bioinformatics. 2011 Jun 15;27(12):1739-40. doi: 10.1093/bioinformatics/btr260. Epub 2011 May 05.

MOTIVATION: Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets.RESULTS: We...

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Liberzon A, Subramanian A, Pinchback R, et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739-40doi: 10.1093/bioinformatics/btr260.
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011). Molecular signatures database (MSigDB) 3.0. Bioinformatics (Oxford, England), 27(12), 1739-40. https://doi.org/10.1093/bioinformatics/btr260
Liberzon, Arthur, et al. "Molecular signatures database (MSigDB) 3.0." Bioinformatics (Oxford, England) vol. 27,12 (2011): 1739-40. doi: https://doi.org/10.1093/bioinformatics/btr260
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011 Jun 15;27(12):1739-40. doi: 10.1093/bioinformatics/btr260. Epub 2011 May 05. PMID: 21546393; PMCID: PMC3106198.
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Reorganizing the protein space at the Universal Protein Resource (UniProt).

Nucleic acids research

[No authors listed]
PMID: 22102590
Nucleic Acids Res. 2012 Jan;40:D71-5. doi: 10.1093/nar/gkr981. Epub 2011 Nov 18.

The mission of UniProt is to support biological research by providing a freely accessible, stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces. UniProt is comprised of four major components, each...

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Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2011;40:D71-5doi: 10.1093/nar/gkr981.
(2012). Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic acids research, 40D71-5. https://doi.org/10.1093/nar/gkr981
"Reorganizing the protein space at the Universal Protein Resource (UniProt)." Nucleic acids research vol. 40 (2012): D71-5. doi: https://doi.org/10.1093/nar/gkr981
Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012 Jan;40:D71-5. doi: 10.1093/nar/gkr981. Epub 2011 Nov 18. PMID: 22102590; PMCID: PMC3245120.
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Molecular mechanistic associations of human diseases.

BMC systems biology

Stegmaier P, Krull M, Voss N, Kel AE, Wingender E.
PMID: 20815942
BMC Syst Biol. 2010 Sep 06;4:124. doi: 10.1186/1752-0509-4-124.

BACKGROUND: The study of relationships between human diseases provides new possibilities for biomedical research. Recent achievements on human genetic diseases have stimulated interest to derive methods to identify disease associations in order to gain further insight into the network...

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Stegmaier P, Krull M, Voss N, et al. Molecular mechanistic associations of human diseases. BMC Syst Biol. 2010;4:124doi: 10.1186/1752-0509-4-124.
Stegmaier, P., Krull, M., Voss, N., Kel, A. E., & Wingender, E. (2010). Molecular mechanistic associations of human diseases. BMC systems biology, 4124. https://doi.org/10.1186/1752-0509-4-124
Stegmaier, Philip, et al. "Molecular mechanistic associations of human diseases." BMC systems biology vol. 4 (2010): 124. doi: https://doi.org/10.1186/1752-0509-4-124
Stegmaier P, Krull M, Voss N, Kel AE, Wingender E. Molecular mechanistic associations of human diseases. BMC Syst Biol. 2010 Sep 06;4:124. doi: 10.1186/1752-0509-4-124. PMID: 20815942; PMCID: PMC2946303.
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Showing 25 to 36 of 109 entries