Although each of the cells in human body carries the same DNA sequence, there are many varieties of cell types and functions. The differences stem from how the sequence of the DNA is interpreted.
Recent developments in single-cell sequencing are allowing researchers to measure which of 20,000 genes in human is active in each cell. With more than 30 trillion cells in our body, the methods offer an unparalleled level of detail that is transforming research in medicine and biology. But when this technique is applied to numerous cells from various tissues, it turns out to be increasingly problematic to process the huge amounts of data and perceive meaningful patterns.
Stein Aerts, a computational biologist and Professor at the University of Leuven, and his team joined forces with bioengineers, IT specialists, and mathematicians to rise to the challenge. They developed a computer program that detects different types of cells based on their patterns of gene expression. Referred to as SCENIC, the program identifies different cell types quickly and accurately.
The researchers’ technique could help develop a cell “atlas” in the human body. The result is expected to become an invaluable source of information for biology and medicine.
The genetic legacy of Neanderthal could influence many things from sunburns to cholesterol and bad habits. There was a time when Neanderthals were considered as mindless brutes. However, that idea has long been proven wrong. Neanderthals, in many ways, were just like humans. They were also superior in some ways. Today, anthropologist know that humans and Neanderthals interbred, leaving humans with a percentage of their DNA.
In the new study, computational biologists Janet Kelso and Michael Dannemann looked at the link between DNA of Neanderthal and human behavior and appearance. Their analysis was broad because it included more than 100,000 individuals. However, it was also limited because all the data came from UK Biobank.
The researchers found the Neanderthal genes determine eye and hair color, sleep time preference, and even how badly you sunburn. It may be genes of Neanderthal that control whether you are a night owl or a morning person. However, according to Kay Prüfer, a researcher at the Max Planck Institute for Evolutionary Anthropology in Leipzig, people should not go accusing Neanderthals of all their woes. She co-authored a different study, in which people living in Western Eurasia were found to carry less Neanderthal gene than people in East Asians.
Prüfer and colleagues conducted a broad, high-quality sequencing of a Neanderthal genome. They studied the bones of a 52,000-year-old Neanderthal woman. They found that genes Neanderthal contribute 1.8 to 2.6% of the total genetic makeup of Eurasian people.
A computational biologist uses theoretical principles of computation to understand biological systems. His/her main duties include research and computer programming in matters relating to biology or biochemistry. Computational biologists have extensive knowledge in computing, statistics, and biology.
Computational biologists work part-time or full-time. However, in most cases, they are employed on a full-time basis. Most computational biologists earn a salary of over $50,000 per year. This salary scale is quite an incentive as compared to what other professionals earn in other fields.
In the field of computational biology, getting a bachelor’s degree is crucial. Usually, professionals get their degrees in biomedical sciences. A student may decide to major in mathematics, computer science or statistics. The extensive education needed in this job prepares candidates with an in-depth knowledge of research, computer programming and the biological aspects of the job.
Once you have the bachelor’s degree, you can decide to proceed to do your master’s degree. After you get the master’s degree, you can then commence your doctoral studies. Doctoral students immerse themselves in their actual job and their school work.
By the time a computational biologist finishes his or her doctoral degrees, he/she has had ample experience and training in the field. A computational biologist may get hired within the pharmaceutical and biotechnological industries or find positions in academia.
For the first time, a team of researchers has sequenced a minute worm that belongs to exclusively asexual species that originated about 18 million years ago. The work reveals how the warm has avoided the evolutionary dead end commonly met by any organism that does not engage in sex.
According to David Fitch, a New York University Biology Professor and one of the co-authors of the research, scientists have been examining how some organisms can survive for many centuries without sex. Fitch believes their study is important in understanding evolutionary genetics since it runs counter to the accepted belief that sexual reproduction is necessary to get rid of deleterious mutations and for survival in a constantly changing environment.
The research carried out in Duke University’s Centre for Genomic and Computational Biology and NYU’s Centre for Genomics and Systems Biology was published in the journal Current Biology.
Diploscapter pachys, the newly sequenced worm, is a minute, free-living roundworm. It is closely related to an organism known as Caenorhabditis elegans that are mainly used for biomedical research. However, Unlike C. elegans, D. pachys is asexual.
The scientists sequenced the D. pachys’ genome to test how its chromosome is structured, whether by fusion or by the loss of many ancestral chromosomes. Their findings were that D. pachys combine six chromosomes of its predecessor into a single chromosome. It then skips meiosis first division so that its progeny retains the parents’ high genetic diversity.
Almost a century ago, researchers uncovered that reducing intake of calorie could extend lifespan in some animal species. However, despite many studies since, scientists have not been able to explain why. Today, researchers at the Lewis Katz School of Medicine at Temple University have gone past that barrier. In an article that was published in Nature Communications on 14th September 2017, these researchers are the first to demonstrate that the speed at which the epigenome varies with age is linked to lifespan in certain species and that restriction of calorie shows this change process, potentially explaining its effects on prolonged existence.
According to Jean-Pierre Issa, MD, Director of the Fels Institute for Cancer Research at LKSOM, the research shows that epigenetic change, which is characterized by losses and gains in DNA methylation in the genome after some time, happens faster in mice than in chimps and faster in chimps than in people. The study helps to explain the reasons why humans live about 70 or 80 years on average, rhesus monkeys about 25 years, and mice 3 to 4 years.
Dr.Issa and his team made their findings after examining patterns in methylation on DNA in blood gathered from individuals of diverse ages of each of three species- human, monkey, and mouse. Mice age ranged from a few months to about three years, chimps from at most three decades, and human from 0 to 86 years. Age-related differences in DNA methylation were examined by deep sequencing technology that shows distinct patterns. Gains in methylation in mature occurred at genomic areas that were not methylated in young individuals.
Scientists at the Helmholtz Zentrum München have created a new method that reconstructs continuous biological processes. The study was published in ‘Nature Communications.’
Today, life science is generating massive data in very short cycles. Making that data suitable and controllable for evaluation is the goal of the researchers at the Helmholtz Zentrum München’s Institute of Computational Biology. With that in mind, Dr. Alexander Wolf and his colleague are attempting to create software that handles this evaluation.
According to Wolf, the study dealt with the issues that software is unable to assign image data to continuous processes. For instance, it is possible to categorize information according to defined classifications, but in developmental biology and disease progression, the limits are rapidly reached since the processes are continuous. To take that into account, the researchers employed methods from machine learning processes. They combined pictures into processes and displayed them in a way that people understand.
To understand the capability of the method, the researchers selected two approaches. In the first experiment, the software was used to reconstruct the continuous cell cycle of leukocytes using pictures from an imaging flow cytometer. In the second example, the scientists reconstructed diabetic retinopathy progress. The software was feed with 30,000 retinas images as sparring partners. Since the software automatically compiles the data into a continuous process, it allows the researchers to predict the progression of disease on a continuous scale.
Computational systems biology is the use of mathematical and systems biology. It aims to develop efficient algorithms, visualization, and communication tools and data structures with the goal of modeling biological systems. Generally, computational systems biology uses simulations of biological systems.
Today, technological advances have a major impact on molecular biology. Developments in experimental methods mean a large volume of sequence, localization and expression data are now gathered by individual investigators. Additionally, large amounts of these data are kept in many private and public databases. At the same time, access to large-scale computing resources is becoming more common in laboratories of molecular biology. Computational systems biology helps us to learn how to leverage these advances in both computational and experimental resources.
To comprehensively understand biological systems, the integration of computational and experimental research is required. Through theoretical exploration and pragmatic modeling, computational biology provides a foundation from which to address scientific questions head-on. A study in computational systems biology allows individuals to solve biological challenges by combining computing, math and a strong base of biological concepts and knowledge. Individuals learn to approach challenges and formulate questions that span biological systems, from cells to genes to cells to ecology to medicine to evolution.
Computational Biology Market in the world is poised to grow at a compound annual growth rate (CAGR) of about 21.7 percent over the next eight years to reach about $11.43B by 2015.
Carried out by Research and Markets, the report, referred to as “Global Computational Biology Market Analysis & Trends – Industry Forecast to 2025”, analyses the market forecasts and estimates of all the given sectors on global and regional levels presented in the scope of the research. The study focuses on leading players, market trends, supply chain trends, key developments, future strategies, and technological innovations. With detailed market assessment across geographies such as Europe, North America, Latin America, Asia Pacific, Middle East and the Rest of the world, the study is an important asset for the future investors, new entrants, and the existing players.
Growing demand for protein sequencing and nucleic acid, growing application of computational biology, increasing initiatives from private organizations and government, and improved collaborative ties between research institutes and companies are the main factors driving the growth of the computational biology market. The major factors limiting the growth of this market, on the other hand, are lack of adequate user-friendly tools and the shortage of skilled professional.
There is a large amount of available data that can be used to predict chemical toxicity without carrying out animal tests, thanks to international data-sharing projects. The enormous volume of these databases makes it difficult to use conventional data-analysis tools when processing them. Recent advances in big-data analytics, however, provide new methods for predictions of chemical toxicity.
Recently, experts gathered at the Indian Institute of Technology Delhi for a national event, “Breaking Barriers through Bioinformatics and Computational Biology.” They shared information on the latest development in computational biology.
PETA India described how companies could use big data to reduce animal testing. It also asked private and government organizations to use big-data analytics methods. A PETA India poster discussed the disadvantages of traditional animal-based methods to determining the toxicity of chemicals, the progress on data-sharing, and how data in public repositories can be used to make models that can predict the toxicity level of chemical compounds. The steps that regulatory and government authorities can take to adopt big data and reduce animal testing were also discussed.
According to PETA India’s Dr. Rohit Bhatia, using big advanced data analytics approach to predict toxicity save money, time, and lives of many animals.
BioUML is an open source and extensible software framework for data analysis from advanced computational biology developed by researchers from the Institute of Systems Biology. The platform is available online and is used in research labs for the discovery of disease origins and prevention. The platform aims at covering all areas of computational applications in systems biology and bioinformatics.
Currently, BioUML include three versions: BioUML Server, BioUML Workbench, and BioUML Web Edition. BioUML Server provides access to data and analysis techniques. BioUML Workbench is a Java application that works standalone for the platform server edition. Lastly, BioUML Web Edition is a web browser that offers most of the BioUML workbench functionality.
Since 2003, the platform has been developed constantly and provides data analysis and visualizations for researchers involved in molecular biology research. It allows scientists to comprehensively describe biological systems function and structure including tools needed to make findings related to metabolomics, transcriptomics, proteomics, and genomics.
High-throughput sequencing and other next-generation sequencing methods create big data. BioUML platform disseminates, study, and produce simulations and visualizations, facilitates parameter fitting and supports numerous other analysis techniques needed to deal with large amounts of data.