HUMA: A platform for the analysis of genetic variation in humans
- Brown, David K, Tastan Bishop, Özlem
- Authors: Brown, David K , Tastan Bishop, Özlem
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/124653 , vital:35642 , https://doi.10.1002/humu.23334
- Description: The completion of the human genome project at the beginning of the 21st century, along with the rapid advancement of sequencing technologies thereafter, has resulted in exponential growth of biological data. In genetics, this has given rise to numerous variation databases, created to store and annotate the ever-expanding dataset of known mutations. Usually, these databases focus on variation at the sequence level. Few databases focus on the analysis of variation at the 3D level, that is, mapping, visualizing, and determining the effects of variation in protein structures. Additionally, these Web servers seldom incorporate tools to help analyze these data. Here, we present the Human Mutation Analysis (HUMA) Web server and database. HUMA integrates sequence, structure, variation, and disease data into a single, connected database. A user-friendly interface provides click-based data access and visualization, whereas a RESTfulWebAPI provides programmatic access to the data. Tools have been integrated into HUMA to allow initial analyses to be carried out on the server. Furthermore, users can upload their private variation datasets, which are automatically mapped to public data and can be analyzed using the integrated tools. HUMA is freely accessible at https://huma.rubi.ru.ac.za.
- Full Text:
- Authors: Brown, David K , Tastan Bishop, Özlem
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/124653 , vital:35642 , https://doi.10.1002/humu.23334
- Description: The completion of the human genome project at the beginning of the 21st century, along with the rapid advancement of sequencing technologies thereafter, has resulted in exponential growth of biological data. In genetics, this has given rise to numerous variation databases, created to store and annotate the ever-expanding dataset of known mutations. Usually, these databases focus on variation at the sequence level. Few databases focus on the analysis of variation at the 3D level, that is, mapping, visualizing, and determining the effects of variation in protein structures. Additionally, these Web servers seldom incorporate tools to help analyze these data. Here, we present the Human Mutation Analysis (HUMA) Web server and database. HUMA integrates sequence, structure, variation, and disease data into a single, connected database. A user-friendly interface provides click-based data access and visualization, whereas a RESTfulWebAPI provides programmatic access to the data. Tools have been integrated into HUMA to allow initial analyses to be carried out on the server. Furthermore, users can upload their private variation datasets, which are automatically mapped to public data and can be analyzed using the integrated tools. HUMA is freely accessible at https://huma.rubi.ru.ac.za.
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Development of Bioinformatics Infrastructure for Genomics Research:
- Mulder, Nicola J, Adebiyi, Ezekiel, Adebiyi, Marion, Adeyemi, Seun, Ahmed, Azza, Ahmed, Rehab, Akanle, Bola, Alibi, Mohamed, Armstrong, Don L, Aron, Shaun, Ashano, Efejiro, Baichoo, Shakuntala, Benkahla, Alia, Brown, David K, Chimusa, Emile Rugamika, Fadlelmola, Faisal M, Falola, Dare, Fatumo, Segun, Ghedira, Kais, Ghouila, Amel, Hazelhurst, Scott, Itunuoluwa Isewon, Segun Jung, Kassim, Samar Kamal, Kayondo, Jonathan K, Mbiyavanga, Mamana, Meintjes, Ayton, Mohammed, Somia, Mosaku, Abayomi, Moussa, Ahmed, Muhammd, Mustafa, Mungloo-Dilmohamud, Zahra, Nashiru, Oyekanmi, Odia, Trust, Okafor, Adaobi, Oladipo, Olaleye, Osamor, Victor, Oyelade, Jellili, Sadki, Khalid, Salifu, Samson Pandam, Soyemi, Jumoke, Panji, Sumir, Radouani, Fouzia, Souiai, Oussama, Tastan Bishop, Özlem
- Authors: Mulder, Nicola J , Adebiyi, Ezekiel , Adebiyi, Marion , Adeyemi, Seun , Ahmed, Azza , Ahmed, Rehab , Akanle, Bola , Alibi, Mohamed , Armstrong, Don L , Aron, Shaun , Ashano, Efejiro , Baichoo, Shakuntala , Benkahla, Alia , Brown, David K , Chimusa, Emile Rugamika , Fadlelmola, Faisal M , Falola, Dare , Fatumo, Segun , Ghedira, Kais , Ghouila, Amel , Hazelhurst, Scott , Itunuoluwa Isewon , Segun Jung , Kassim, Samar Kamal , Kayondo, Jonathan K , Mbiyavanga, Mamana , Meintjes, Ayton , Mohammed, Somia , Mosaku, Abayomi , Moussa, Ahmed , Muhammd, Mustafa , Mungloo-Dilmohamud, Zahra , Nashiru, Oyekanmi , Odia, Trust , Okafor, Adaobi , Oladipo, Olaleye , Osamor, Victor , Oyelade, Jellili , Sadki, Khalid , Salifu, Samson Pandam , Soyemi, Jumoke , Panji, Sumir , Radouani, Fouzia , Souiai, Oussama , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148239 , vital:38722 , DOI: 10.1016/j.gheart.2017.01.005
- Description: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community.
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- Authors: Mulder, Nicola J , Adebiyi, Ezekiel , Adebiyi, Marion , Adeyemi, Seun , Ahmed, Azza , Ahmed, Rehab , Akanle, Bola , Alibi, Mohamed , Armstrong, Don L , Aron, Shaun , Ashano, Efejiro , Baichoo, Shakuntala , Benkahla, Alia , Brown, David K , Chimusa, Emile Rugamika , Fadlelmola, Faisal M , Falola, Dare , Fatumo, Segun , Ghedira, Kais , Ghouila, Amel , Hazelhurst, Scott , Itunuoluwa Isewon , Segun Jung , Kassim, Samar Kamal , Kayondo, Jonathan K , Mbiyavanga, Mamana , Meintjes, Ayton , Mohammed, Somia , Mosaku, Abayomi , Moussa, Ahmed , Muhammd, Mustafa , Mungloo-Dilmohamud, Zahra , Nashiru, Oyekanmi , Odia, Trust , Okafor, Adaobi , Oladipo, Olaleye , Osamor, Victor , Oyelade, Jellili , Sadki, Khalid , Salifu, Samson Pandam , Soyemi, Jumoke , Panji, Sumir , Radouani, Fouzia , Souiai, Oussama , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148239 , vital:38722 , DOI: 10.1016/j.gheart.2017.01.005
- Description: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community.
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MD-TASK: a software suite for analyzing molecular dynamics trajectories
- Brown, David K, Penkler, David L, Amamuddy, Olivier S, Ross, Caroline J, Atilgan, Ali R, Atilgan, Canan, Tastan Bishop, Özlem
- Authors: Brown, David K , Penkler, David L , Amamuddy, Olivier S , Ross, Caroline J , Atilgan, Ali R , Atilgan, Canan , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125138 , vital:35735 , https://doi.10.1093/bioinformatics/btx349
- Description: Molecular dynamics (MD) determines the physical motions of atoms of a biological macromolecule in a cell-like environment and is an important method in structural bioinformatics. Traditionally, measurements such as root mean square deviation, root mean square fluctuation, radius of gyration, and various energy measures have been used to analyze MD simulations. Here, we present MD-TASK, a novel software suite that employs graph theory techniques, perturbation response scanning, and dynamic cross-correlation to provide unique ways for analyzing MD trajectories.
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- Authors: Brown, David K , Penkler, David L , Amamuddy, Olivier S , Ross, Caroline J , Atilgan, Ali R , Atilgan, Canan , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125138 , vital:35735 , https://doi.10.1093/bioinformatics/btx349
- Description: Molecular dynamics (MD) determines the physical motions of atoms of a biological macromolecule in a cell-like environment and is an important method in structural bioinformatics. Traditionally, measurements such as root mean square deviation, root mean square fluctuation, radius of gyration, and various energy measures have been used to analyze MD simulations. Here, we present MD-TASK, a novel software suite that employs graph theory techniques, perturbation response scanning, and dynamic cross-correlation to provide unique ways for analyzing MD trajectories.
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PRIMO: an interactive homology modeling pipeline
- Hatherley, Rowan, Brown, David K, Glenister, Michael, Tastan Bishop, Özlem
- Authors: Hatherley, Rowan , Brown, David K , Glenister, Michael , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148282 , vital:38726 , doi: 10.1371/journal.pone.0166698
- Description: The development of automated servers to predict the three-dimensional structure of proteins has seen much progress over the years. These servers make calculations simpler, but largely exclude users from the process. In this study, we present the PRotein Interactive MOdeling (PRIMO) pipeline for homology modeling of protein monomers. The pipeline eases the multi-step modeling process, and reduces the workload required by the user, while still allowing engagement from the user during every step. Default parameters are given for each step, which can either be modified or supplemented with additional external input. PRIMO has been designed for users of varying levels of experience with homology modeling. The pipeline incorporates a user-friendly interface that makes it easy to alter parameters used during modeling.
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- Authors: Hatherley, Rowan , Brown, David K , Glenister, Michael , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148282 , vital:38726 , doi: 10.1371/journal.pone.0166698
- Description: The development of automated servers to predict the three-dimensional structure of proteins has seen much progress over the years. These servers make calculations simpler, but largely exclude users from the process. In this study, we present the PRotein Interactive MOdeling (PRIMO) pipeline for homology modeling of protein monomers. The pipeline eases the multi-step modeling process, and reduces the workload required by the user, while still allowing engagement from the user during every step. Default parameters are given for each step, which can either be modified or supplemented with additional external input. PRIMO has been designed for users of varying levels of experience with homology modeling. The pipeline incorporates a user-friendly interface that makes it easy to alter parameters used during modeling.
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Role of structural bioinformatics in drug discovery by computational SNP analysis: analyzing variation at the protein level
- Brown, David K, Tastan Bishop, Özlem
- Authors: Brown, David K , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125921 , vital:35832 , https://doi.10.1016/j.gheart.2017.01.009
- Description: With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps toward an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as genome-wide association studies and candidate gene association studies. However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as high throughput sequencing. In this paper, we discuss the contributions of structural bioinformatics to drug discovery, focusing particularly on the analysis of nonsynonymous single nucleotide polymorphisms. We conclude by suggesting a protocol for future analyses of the structural effects of nonsynonymous single nucleotide polymorphisms on proteins and protein complexes.
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- Authors: Brown, David K , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/125921 , vital:35832 , https://doi.10.1016/j.gheart.2017.01.009
- Description: With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps toward an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as genome-wide association studies and candidate gene association studies. However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as high throughput sequencing. In this paper, we discuss the contributions of structural bioinformatics to drug discovery, focusing particularly on the analysis of nonsynonymous single nucleotide polymorphisms. We conclude by suggesting a protocol for future analyses of the structural effects of nonsynonymous single nucleotide polymorphisms on proteins and protein complexes.
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Structure-based analysis of single nucleotide variants in the renin-angiotensinogen complex:
- Brown, David K, Olivier, Sheik Amamuddy, Tastan Bishop, Özlem
- Authors: Brown, David K , Olivier, Sheik Amamuddy , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/147994 , vital:38700 , https://doi.org/10.1016/j.gheart.2017.01.006
- Description: The renin-angiotensin system (RAS) plays an important role in regulating blood pressure and controlling sodium levels in the blood. Hyperactivity of this system has been linked to numerous conditions including hypertension, kidney disease, and congestive heart failure. Three classes of drugs have been developed to inhibit RAS. In this study, we provide a structure-based analysis of the effect of single nucleotide variants (SNVs) on the interaction between renin and angiotensinogen with the aim of revealing important residues and potentially damaging variants for further inhibitor design purposes.
- Full Text:
- Authors: Brown, David K , Olivier, Sheik Amamuddy , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/147994 , vital:38700 , https://doi.org/10.1016/j.gheart.2017.01.006
- Description: The renin-angiotensin system (RAS) plays an important role in regulating blood pressure and controlling sodium levels in the blood. Hyperactivity of this system has been linked to numerous conditions including hypertension, kidney disease, and congestive heart failure. Three classes of drugs have been developed to inhibit RAS. In this study, we provide a structure-based analysis of the effect of single nucleotide variants (SNVs) on the interaction between renin and angiotensinogen with the aim of revealing important residues and potentially damaging variants for further inhibitor design purposes.
- Full Text:
The role of structural bioinformatics in drug discovery via computational SNP analysis–a proposed protocol for analyzing variation at the protein level:
- Brown, David K, Tastan Bishop, Özlem
- Authors: Brown, David K , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/162914 , vital:40996 , doi: 10.1016/j.gheart.2017.01.009
- Description: With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps towards an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as Genome-Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS). However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as High Throughput Sequencing (HTS).
- Full Text:
- Authors: Brown, David K , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/162914 , vital:40996 , doi: 10.1016/j.gheart.2017.01.009
- Description: With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps towards an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as Genome-Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS). However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as High Throughput Sequencing (HTS).
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