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Computational Biology and Bioinformatics

Hartford, CT, United States

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News Article | October 10, 2016
Site: www.rdmag.com

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture. For the first time, a new tool developed at the Department of Energy's (DOE's) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer's spread throughout the brain. Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data--like heat maps, node link diagrams and anatomical views--to provide context for brain connectivity data. "The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before," says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis. "Other tools tend to look at abstract or statistical network connections but don't do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context," says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool's functionality. A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool's development. As a neuroscientist at UCSF's Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer's and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts. "We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That's the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections," says Brown. To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. "For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c," Brown adds. Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they'd also look at fMRI data that had been reduced to a static network diagram. "The problem with this analysis process is that it's all static. If I wanted to explore another region of the brain, which would be a different pattern, I'd have to input a whole different set of data and create another set of static images," says Brown. But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views. "Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn't realize existed before in the dataset. Every time I use it, I find something surprising in the data," says Brown. "It is also incredibly valuable for researchers who don't know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition." The idea for Brain Modulyzer initiated when Berkeley Lab's Weber and Seeley met at the "Computational Challenges for Precision Medicine" in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries--a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013. "At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools," says Brown. "The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch." After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called "Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data." Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health. Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. "We want to make sure that this tool is useful to the community, so we will keep iterating on it," says Brown. "We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time."


News Article | October 12, 2016
Site: www.biosciencetechnology.com

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture. For the first time, a new tool developed at the Department of Energy's (DOE's) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer's spread throughout the brain. Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data--like heat maps, node link diagrams and anatomical views--to provide context for brain connectivity data. "The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before," says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis. "Other tools tend to look at abstract or statistical network connections but don't do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context," says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool's functionality. A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool's development. As a neuroscientist at UCSF's Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer's and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts. "We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That's the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections," says Brown. To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. "For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c," Brown adds. Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they'd also look at fMRI data that had been reduced to a static network diagram. "The problem with this analysis process is that it's all static. If I wanted to explore another region of the brain, which would be a different pattern, I'd have to input a whole different set of data and create another set of static images," says Brown. But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views. "Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn't realize existed before in the dataset. Every time I use it, I find something surprising in the data," says Brown. "It is also incredibly valuable for researchers who don't know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition." The idea for Brain Modulyzer initiated when Berkeley Lab's Weber and Seeley met at the "Computational Challenges for Precision Medicine" in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries--a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013. "At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools," says Brown. "The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch." After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called "Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data." Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health. Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. "We want to make sure that this tool is useful to the community, so we will keep iterating on it," says Brown. "We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time."


News Article | October 10, 2016
Site: www.chromatographytechniques.com

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture. For the first time, a new tool developed at the Department of Energy’s (DOE’s) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer’s spread throughout the brain. Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data—like heat maps, node link diagrams and anatomical views—to provide context for brain connectivity data. “The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before,” says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis. “Other tools tend to look at abstract or statistical network connections but don’t do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context,” says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool’s functionality. A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool’s development. As a neuroscientist at UCSF’s Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer’s and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts. “We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That’s the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections,” says Brown. To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. “For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c,” Brown adds. Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they’d also look at fMRI data that had been reduced to a static network diagram. “The problem with this analysis process is that it’s all static. If I wanted to explore another region of the brain, which would be a different pattern, I’d have to input a whole different set of data and create another set of static images,” says Brown. But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views. “Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn’t realize existed before in the dataset. Every time I use it, I find something surprising in the data,” says Brown. “It is also incredibly valuable for researchers who don’t know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition.” The idea for Brain Modulyzer initiated when Berkeley Lab’s Weber and Seeley met at the “Computational Challenges for Precision Medicine” in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries—a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013. “At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools,” says Brown. “The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch.” After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called “Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data.” Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health. Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. “We want to make sure that this tool is useful to the community, so we will keep iterating on it,” says Brown. “We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time.” In addition to Brown, Weber, Murugesan and Seeley other authors on the paper are Bernd Hamann (UC Davis), Andrew Trujillo (UCSF) and Kristopher Bouchard (Berkeley Lab).


A researcher at Case Western Reserve University is developing a low-cost, portable prototype designed to detect tainted medicines and food supplements that otherwise can make their way to consumers. The technology can authenticate good medicines and supplements. "There is a big problem with counterfeit and substandard medicines in poorer countries, particularly in Africa and Asia," said Soumyajit Mandal, assistant professor in the Department Electrical Engineering and Computer Science in the Case School of Engineering. "In the U.S., the biggest problem is with various dietary supplements." Mandal and his collaborators are developing a small, box-like detector that has been preliminary tested in field trials. "The work builds on—and improves—a related project introduced in Europe a few years ago to create a portable, low-cost detector for medicines," he said. Mandal said the detector he and his colleagues are developing is much more flexible (capable of analyzing a wide variety of medicines and dietary supplements), and more sensitive (capable of measuring smaller quantities). Mandal is the principal investigator of the research and co-author of an associated paper to be published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, a bimonthly peer-reviewed scientific journal. Research participants are Professor Swarup Bhunia at the University of Florida, in Gainesville, Fla., and Research Fellow Jamie Barras and Professor Kaspar Althoefer, both at King's College London. "Current results are very promising and have advantages over competing methods," Mandal said. "The required instrumentation is simple and low-cost, compared to other analytical techniques, such as optical spectroscopy." The paper, titled "Authentication of Medicines using Nuclear Quadrupole Resonance Spectroscopy," links their research to the concept of safe medicines "as a human right." In 2013, the United Nations Human Rights Council adopted a resolution focused on access to medicines "that are affordable, safe, effective and of good quality." "We think our technology will have an important positive impact on public health by enabling consumers to directly authenticate the contents packets or bottles without having to send samples to an analytical chemistry lab," Mandal said. A medicine or dietary supplement might be incorrectly labeled for a variety of reasons—intentional fraud, poor manufacturing practices and degradation due to poor storage or post-expiration date. The key to detection is to know the proper active pharmaceutical ingredients (API) in a medicine or supplement, so technicians can determine whether a pill or powder is what it appears to be. The technology being developed in Mandal's lab does not authenticate liquids. The device uses Nuclear Quadrupole Resonance (NQR) spectroscopy, a non-invasive and non-destructive analytical technique for medicines and supplements in pill or powder form. Most chemical elements have nuclei that generate NQR signals. Almost all medicines have API with NQR-active nuclei. Mandal's research team proposes a "chemometric passport approach" for quality assurance. Data on packaged medicines will be derived from a spectroscopic analysis performed at the point of manufacture. The contents of the packet will later be authenticated by matching the results of another spectroscopic analysis using unique chemical identifiers from a reference spectrum. Authentication information can be accessed either from a secure database stored in the cloud, or from information encoded directly within the product barcode. The absence of a match triggers a "contents don't match the label" alarm on the testing device. Mandal said that capability would be particularly useful at customs checkpoints and postal sorting offices when a barcode might not be visible. One day, he said, a person might be able to test his or her own medicines or supplements at home, which would have a direct effect on public health. The research is showing that NQR isn't sensitive to pill coatings and non-metallic packaging material, Mandal said. "Part of what we are proposing is to take this product and do a systematic survey of how much misidentification there is out there," Mandal said. "We need more data to understand the extent of the problem. We are recruiting people willing to try our prototype." More information: Cheng Chen et al. Authentication of medicines using nuclear quadrupole resonance spectroscopy, IEEE/ACM Transactions on Computational Biology and Bioinformatics (2015). DOI: 10.1109/TCBB.2015.2511763


Horvat E.-A.,The Interdisciplinary Center | Horvat E.-A.,University of Kaiserslautern | Da Zhang J.,Computational Biology and Bioinformatics | Uhlmann S.,German Cancer Research Institute | And 3 more authors.
PLoS ONE | Year: 2013

Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis. © 2013 Horvat et al.


Liu Z.,Sun Yat Sen University | Hu Z.,Sun Yat Sen University | Hu Z.,Nankai University | Pan X.,Yale University | And 19 more authors.
Human Molecular Genetics | Year: 2011

Parthenogenetic embryonic stem cells (pESCs) have been generated in several mammalian species from parthenogenetic embryos that would otherwise die around mid-gestation. However, previous reports suggest that pESCs derived from in vivo ovulated (IVO) mature oocytes show limited pluripotency, as evidenced by low chimera production, high tissue preference and especially deficiency in germline competence, a critical test for genetic integrity and pluripotency of ESCs. Here, we report efficient generation of germlinecompetent pESC lines (named as IVM pESCs) from parthenogenetic embryos developed from immature oocytes of adult mouse ovaries following in vitro maturation (IVM) and artificial activation. In contrast, pESCs derived from IVO oocytes show defective germline competence, consistent with previous reports. Further, IVM pESCs resemble more ESCs from fertilized embryos (fESCs) than do IVO pESCs on genomewide DNA methylation and global protein profiles. In addition, IVM pESCs express higher levels of Blimp1, Lin28 and Stella, relative to fESCs, and in their embryoid bodies following differentiation. This may indicate differences in differentiation potentially to the germline. The mechanisms for acquisition of pluripotency and germline competency of IVM pESCs from immature oocytes remain to be determined. © The Author 2011. Published by Oxford University Press. All rights reserved.

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