For a long time, genomics have been a “read-only” science. However, researchers have developed a tool that can easily and quickly delete DNA in living cells. Published in PLOS Computational Biology, this software will boost scientists’ effort to understand the vast areas of non-coding DNA. Consequently, this may lead to discovery of new genes that cause disease and potential new drugs.
CRISPR-Cas9 is an innovative method that is used to edit genomes. The aim of most studies that use this technique is to silence protein-coding genes (the most-studied area of our genome). It is important, however, to know that 99 percent of DNA does not take part in encoding protein. The “non-coding DNA”, also known as “Dark Matter”, is known to be important for understanding aspects of human biology such as evolution and disease.
Recently, the Johnson lab developed a tool called “DECKO”. The tool can be used to delete a desired part of non-coding DNA. One unique advantage of the tool is that it uses binary individual sgRNAs, working like two scissors that cut out a section of DNA. The method was extensively adopted, but since there was no software to design sgRNAs pairs that are needed, designing deletion tests was slow.
In response to this, Carlos Pulido and his colleagues developed software known as CRISPETa that is used to design CRISPR deletion experiments. CRISPETa receives instructions from the user who tells it the part they want to delete. Then, the software sends a set of optimised sgRNAs pairs that are directly used by researchers. CRISPETa designs effectively delete desired targets in animal cells.
In computational biology, a sequence alignment is a method of arranging the sequences of DNA, protein, or RNA to identify areas of similarity that may be as a result of revolutionary, functional, or structural relationships between the sequences. Typically, aligned sequences of amino acids or nucleotide residues are represented as rows within a matrix. Between the residues, gaps are inserted so that similar or identical characters are arranged in successive columns.
In an alignment, if two sequences share a common ancestor, disparities are construed as point mutations and gaps are interpreted as indels (deletion or insertion mutations) introduced in both or one lineages in the period since they separated from one another. In protein sequence alignment, the degree of resemblance between amino acids that occupy a specific position in the sequence is construed as a rough indication of how conserved a given region is among lineages.
The presence of only conservative substitutions or the absence of substitutions in a given region of sequence shows that that region has functional and structural importance. Although RNA or DNA nucleotides base are more analogous to each other compared to amino acids, base pair conservation can suggest a similar structural or functional role.
According to a study published in PLOS Computational Biology, a new mathematical model may help explain what brought the evolution of big brains in human and other animals. Animals with large brains also have a high cognitive ability, but the factors that cause the evolution of large brains remain unclear. Researchers have hypothesized social, cultural, and ecological factors could each play a role. Although these hypotheses have not been mathematically formalized, doing so would enable researchers to refine their tests.
To address the issue, a mathematical model was developed by Mauricio González-Forero of University of Lausanne and colleagues. The model predicts how big the human brain should be at various stages of a person’s life, depending on several evolutionary scenarios.
One assumption of the model is that the brain uses energy on cognitive skills that enable a person to extract energy from food, which in turn enables the brain to develop. Given natural selection, the model predicts the amount of energy that is used to support brain development at different ages under diverse biological settings.
The scientists used the model to test a scenario in which cultural factors and social interactions are excluded, showing the impact of ecological factors alone. In the setting, a human hunts or gathers food alone. However, it may be helped by its mother when it is still young.
Under those conditions, the hypothetical brain of human being grew as large as early humans brains are believed to have grown. In addition, the slow growth rate matched the modern human brains. The results run counter to a common believe that cultural and social influences are needed to achieve these growth rates and sizes.
A team of scientists have come up with a new method that identify the cause of irregular electrical ‘storm waves’ in the heart. Published in PLOS Computational Biology, this new research could have great implications for treating killer cardiac disease in the future.
Cause by irregular electrical ‘storm waves’ , Atrial Fibrillation is among the common forms of abnormal heart rhythm, and is known for causing stroke because it increase the risks of blood clots inside the heart. It occurs in about 1-2 percent of people, and previous studies have shown it is rising in the developed world. Since this increase may have major societal and economic implications, there is urgent need for more effective treatment methods for the condition.
To diagnose and treat atrial fibrillation, it is important to identify its origin. The current methods use a catheter to separate the storm waves. However, it is very difficult to identify the source of the waves because this is extremely invasive surgery.
In the current study, the researchers from the University of Hull and the University of Manchester used a 64-lead electrocardiogram (ECG) vest and a virtual human heart-torso to study the relationship between the features of the ECG signals and the origin of the storm waves. They used features of the atrial activation and the signals to develop a method that pin down the position of Atrial Fibrillation non-invasively. The method also identified different types of condition.
Apart from reading the information in the human genome and understanding how it works, scientists aim to know the ins and outs of each element in in human genome. Many projects and laboratories are devoted to get an international view of the functional areas of the genome and to identify the cells types where genes are active.
Interestingly, only a small percentage of the human genome (around 2 percent) has genes encoding for proteins. The remaining 98 percent is important for regulation, meaning it controls where and when genes are active. This big portion of the genome makes RNA molecules which differ in size, function, and structure. As the different types of RNA molecules interact with proteins in different ways, efforts have been put into studying them. Until recently, computational tools to handle very long RNA sequences were not available.
In a recent article that was published in Nature Methods, scientists at the Centre for Genomic Regulation in Barcelona, in collaboration with researchers at EMBL’s site in Monterotondo and the California Institute of Technology, introduced a new computational tool that predicts the interaction of proteins with long non-coding RNAs.
Global Score, the new computational tool, allows researchers to predict where, along non-coding RNA sequence, a protein will form a physical contact. To do that, the algorithm integrates both the global tendency of the protein to bind a specific RNA and the local features of such a binding.