BIOINFORMATIC TOOLS

[Virtual Presenter] Welcome everyone. Today, we have the great pleasure to discuss the various bioinformatics tools used to predict loss of function or pathogenic mutations and explore the effects, types, and importance of these mutations. Let's jump into the presentation and broaden our understanding of this critical topic..

[Audio] The research team is composed of four people: Alex-Ann Burrell (ID number 2100398), Nathan Clunis (ID number), Kevin Brown (ID number) and Aaliya Burrell (ID number 2008272). The team will be investigating bioinformatics tools for predicting the effect of loss of function or pathogenic mutations..

[Audio] Bioinformatics tools provide a comprehensive approach to predicting the effects of mutations. These tools enable researchers to assess how a mutation might affect the function of a gene or its potential to become pathogenic. By gaining insight into the biological basis of these tools, researchers can better predict mutation effects and draw comparisons between different tools. In this presentation, we will cover the bioinformatics tools used for the prediction of mutations known to cause loss of function or pathogenic effects..

[Audio] Mutations are changes in the genetic material, the DNA, of an organism. Types of mutations can range from single nucleotide changes to large deletions, duplications, or insertions. Studying these mutations can help to determine if an organism has a higher risk of developing a genetic disorder or if it can help increase disease resistance. Bioinformatics tools are used to predict the effects of these mutations, whether it be a loss of function or a pathogenic mutation. " Mutations are changes in genetic material that can range from a single nucleotide change up to larger deletions, duplications, or insertions. They can have a major impact on an organism, such as increasing the risk of developing a genetic disorder or helping to make an organism more resistant to diseases. To understand the effects of mutations and how they can change an organism, bioinformatics tools are used in the prediction of the effects of the mutations, such as loss of function or pathogenic mutations..

[Audio] Mutation of DNA is an essential process that has an effect on all living organisms. Fitzgerald & Rosenberg (2019) define mutations as any change in the genome's sequence or the manner in which the change occurs. Bioinformatics can be used to examine the consequences of these mutations, supplying information about the function of the being and what modifications may be harmful. In the following paragraphs, we will study the various approaches employed in anticipating the lack of variant or destructive mutations..

[Audio] Mutations are a natural occurrence in the cycle of life, though their impact may vary. This slide will explore the two main kinds of mutations - somatic and germline - including the range of DNA alteration, the influence on proteins, the kinds of cells affected and the nucleotide modifications..

[Audio] Bioinformatics tools can provide a deep insight into the mutation and its influence on the genetic structure of an individual. Examining the coding sequences of the mutated gene can give us a comprehension of the potential for loss of function or pathogenicity caused by the mutation. By means of detailed consideration, bioinformatic tools are able to anticipate the effects of the mutation in an individual, enabling us to determine which course of treatment is most applicable for the individual concerned..

[Audio] Point mutations, referred to as single nucleotide polymorphisms, are a common type of mutation. These happen when one base pair is inserted, removed, or changed. These can cause a loss of function or a pathogenic mutation, so predicting them is vital. Chromosomal mutations such as duplication, translocation, and inversion can also result in a loss of function or be pathogenic. Bioinformatics tools help us identify and predict these mutations, so that their effect can be better understood..

[Audio] Mutations can occur in a gene sequence. A frameshift mutation happens when the base sequence of nucleotides is changed, impacting the reading frame. This could result in incorrect amino acid sequences or a stop codon. Substitution is when one nucleotide is replaced by another. It can also refer to when one amino acid is switched for another in a protein..

[Audio] Bioinformatics tools can be used to predict loss of function and pathogenic mutations resulting from non-synonymous and synonymous alterations in proteins. Non-synonymous mutations involve changing the original amino acid in a protein to another, which may be known as a missense or nonsense mutation. These mutations can code for stop codons, thereby ending the protein's longevity. Synonymous mutations, on the other hand, change the DNA sequence that codes for the amino acid in the protein without altering the amino acid itself. This is called a silent mutation and it usually occurs in the third nucleotide of a codon. This kind of mutation will cause the same amino acid to be expressed in a different codon, e.g. GGT, GGA, GGC and GGG in the amino acid glycine..

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[Audio] Bioinformatics tools are widely used by researchers to predict loss of function or pathogenic mutations. This slide shows an example of a mutation, where the original DNA sequence is found at the top. There are three types of mutations: Frameshift, Silent and Missense. A Frameshift mutation is demonstrated by inserting UGA (Leucine) in the middle of the DNA sequence, thus altering the amino acids that follow. A Silent mutation is when UGA is changed but is still coding for Leucine, therefore leaving the amino acid sequence unchanged. The Missense mutation is represented by changing UGA to Valine. Lastly, the Nonsense mutation replaces UGA with a stop codon, leading to a truncated protein..

[Audio] Mutations are key to the evolution process, and can be used to benefit society. Through bioinformatics, researchers can learn more about how changes to the genetics cause conditions and illnesses. By studying how mutations lead to loss of function or cause problems, we can gain a more detailed view of genetic diseases, and use that to make new treatments and ways to stop them, so people with these diseases can have a better life..

[Audio] Bioinformatics tools play a crucial role in the prediction of loss of function or pathogenic mutations. By mining existing databases of known mutations and the biological information associated with them, these tools enable us to gain insights into which mutations are likely to be pathogenic and how they may impact an individual's health or biology. This helps us in gaining a greater understanding of the effects of mutations and the implications they may have on both individual patients and medical practices in general..

[Audio] Bioinformatics tools are utilized by researchers for predicting potential mutations in the DNA and the outcomes of these mutations. To construct the algorithms used for the analysis, it necessitates accessing to a massive amount of biological data. These tools offer important insight into a person's genetic structure and can be used for appraising the possible influence of a mutation on the body's operations. By perceiving the significance of multiple elements, biologists can have a better comprehension of the effects of a mutation and make more accurate predictions..

[Audio] Bioinformatic tools offer powerful and sophisticated methods to recognize how genetic mutations can influence the manifestation of a specific gene. Through the comparison of sequences, databases, and models, we can acquire greater comprehension of the influence a potential mutation has on the genes it impacts. By learning the full implications an alteration has on the gene product or signaling network, we can start to make better-informed decisions in regards to treatments for these mutations. This is a highly useful tool in the forecast of loss of function or pathogenic mutations..

[Audio] PolyPhen-2 is an incredibly useful tool that employs a variety of biological information to accurately predict the functional impact of protein mutations. It uses the sequence homology and protein family database annotations and 3D structures from Protein Data Bank to make this assessment. This data when used in conjunction with PolyPhen-2 can accurately inform us on the impact of a mutation, providing invaluable insight into potential treatments or preventative measures..

[Audio] Bioinformatics tools have revolutionized the way we approach the study of genetic variants. One of the most important of these tools is Mutation Assessor, which helps identify potential loss of function or pathogenic mutations. Mutation Assessor considers multiple aspects such as evolutionary conservation and structural features of proteins, to accurately predict the potential impact of a mutation. It was designed to be a fast and reliable predictor of the functional significance of mutations, which leads to better interpretation of genomics data..

[Audio] SIFT is a tool created for the purpose of studying the repercussions of mutations on a protein's behavior. It is based on the ideas of sequence homology and the physicochemical properties of amino acids. By considering the sequences of the proteins and the characteristics of their amino acids, SIFT can anticipate the effects certain mutations can have on the protein, whether it could be pathogenic or lead to a decrease in its capability. This method assists researchers in comprehending how alterations in a protein's structure can shape its function..

[Audio] PolyPhen is a bioinformatic tool used to predict the consequences of genetic mutations. The tool uses a machine-learning algorithm based on Bayesian statistics to identify whether a given mutation is benign or deleterious. Furthermore, PolyPhen generates a score indicating the predicted impact the mutation will have on protein function. With the help of biological information, the algorithms of PolyPhen can be used to make more precise predictions about the effects of loss-of-function or pathogenic mutations..

[Audio] Mutation Assessor is a powerful bioinformatic tool that can provide useful information about a mutation's functional impact. Rather than looking at the mutation in isolation, the tool takes into account factors such as evolutionary conservation and sequence variability at the exact position of the mutation. This makes it a strong resource for predicting loss of function or pathogenic mutations..

[Audio] Bioinformatics tools have the potential to predict loss of functions and pathogenic mutations. An example is the SIFT algorithm, which utilizes a sequence-based technique and evaluates the extent of maintenance at a given position. It can determine if a mutation is acceptable or not. Various algorithms have been developed to use with SIFT..

[Audio] Bioinformatics tools have radically altered the arena of molecular biology. Polyphen, Sift, and Mutation Assessor are three of the most employed tools in the forecast of loss of action and pathogenic mutations. Polyphen unites data from a variety of sources into one algorithm, making it possible to precisely ascertain the constructive or detrimental nature of a certain mutation. Support Vector Machine and Naive Bayes classifiers refine the data more carefully to decide the effects of the mutation. All of this information is employed to offer a precise prediction of the mutation's potential effect on the organism..

[Audio] Mutation Assessor and SIFT are two widely employed bioinformatics tools for predicting the effects of mutations on proteins' functionality. Mutation Assessor employs sequence alignment, sequence entropy and functional annotations to evaluate mutations' influence on proteins, while SIFT applies sequence-based features and a sequence homology method to assess the impact of substitutions on proteins' performance..

[Audio] I'd like to talk to you about exploring bioinformatics tools used in the prediction of loss of function or pathogenic mutations. Polyphen uses a machine learning algorithm, based on Bayesian statistics, to classify mutations as either benign or deleterious. This algorithm assigns a score reflecting the predicted impact on protein function. Let's take a deeper dive into how Polyphen works and the types of mutations it can accurately identify. Additionally, we will explore the algorithms used in SIFT and Mutation Assessor..

[Audio] Slide 26 examines the algorithms used in Polyphen, SIFT and Mutation Assessor. The SIFT algorithm employs a sequence-based approach to determine whether or not a mutation is tolerated. On the other hand, Mutation Assessor utilizes a position-specific approach, focusing on the sequence context and the exact spot of the mutation. Lastly, Polyphen considers evolutionary conservation and sequence variability to evaluate the functional effect..

[Audio] Looking at the factors considered when using the three Bioinformatics tools, PolyPhen, Sift and Mutation Assessor to predict the pathogenic potential of loss of function or mutations, these tools consider position-specific scoring, evolutionary conservation and structural features when calculating the score. For example, PolyPhen takes into account the specific position of the mutation in the protein sequence, the evolutionary conservation of the amino acid in question, and structural features such as the location of the mutation in protein domains..

[Audio] SIFT is a bioinformatics tool used to evaluate the loss of function associated with pathogenic mutations. To do this, it takes into account sequence homology, alignment quality, and type of amino acid substitution. Sequence homology examines the similarity of amino acids at a given position and its corresponding homologs in other species. Alignment quality measures the accuracy of the multiple sequence alignment to get an accurate prediction. Lastly, the type of amino acid substitution refers to the range of moderate to radical differences in chemical make-up. All of these elements are incorporated to figure out the score in SIFT, which is used to assess the loss of function or pathogenicity of mutations..

[Audio] Mutation Assessor is a bioinformatic tool designed to forecast the likely effect of a mutation on the protein structure and function. In its assessment, it takes into account parameters such as the location of the mutation in the protein sequence, the conservation of the amino acid position across various species, and structural characteristics. The score computed by Mutation Assessor is based on supplementary factors like those employed by PolyPhen and SIFT..

[Audio] Bioinformatics tools have drastically changed how we investigate gene function and forecast potential loss of function and pathogenic mutations. This slide will present five mutations from four different genes. Mutations of genes can account for the emergence of human diseases, and understanding these mutations can further our comprehension of the molecular pathways that lead to a particular disease. Once we understand these pathways, medical researchers are enabled to establish finely tuned treatments for the condition..

[Audio] We will examine BRCA2 and BRCA2 DNA repair associated cyclin dependent kinase inhibitor 2A, as well as BRCA1 and BRCA1 DNA repair associated mutS homolog 2 on slide number 31 of our presentation. Our research aims to explore bioinformatics tools used in the prediction of loss of function or pathogenic mutations. Come back for more information about our research..

[Audio] A BRCA1 gene is a DNA repair associated gene that produces Breast Cancer type 1 susceptibility protein. This gene has a size of 126033 base pairs and is located on the q arm of Chromosome 17 at position 21.31. Its purpose is to ensure genomic stability, and it codes for a tumor suppressing protein. It is linked to the disease of Breast Cancer..

[Audio] Bioinformatic tools are key to predict the effects of gene mutations in humans, and today I'll be looking at five BRCA1 mutations: VAR_020680, VAR_070460, VAR_070463, VAR_063899, and VAR_007756. These mutations are characterized by the replacement of amino acids in these genes, such as Glutamic Acid with Lysine, Aspartic Acid with Tyrosine, Proline with Histidine, Methionine with Threonine, and Leucine with Serine. This replacement can be classified as either a conservative or non-conservative missense mutation..

[Audio] Bioinformatics is allowing us to explore the complex sequence of human DNA at an unprecedented level. As part of this exploration, tools are being developed to predict the loss of function or pathogenic mutations which may be associated with certain diseases. One such example is BRCA2. This is a DNA repair associated protein which provides genomic stability and codes for a tumor suppressing protein. The size of BRCA2 is 84760 base pairs and its location is 13.1 on the q arm of chromosome 13. It is associated with Breast Cancer type 2 susceptibility..

[Audio] Bioinformatics tools are essential in helping to identify certain mutations in genes with potential disease-causing effects. We will look at five mutations in the BRCA2 gene, each of which is a substitution of an amino acid with a different one. In VAR_028167, Glycine [G] at position 25 is replaced with Arginine [R], and is classified as a Missense Non-Conservative mutation. Similarly, in VAR_005085, Phenylalanine [F] at position 32 is replaced with Leucine [L], a Missense Conservative mutation. Moving to VAR_020705, Tyrosine [Y] at position 42 is replaced with Cysteine [C], another Missense Non-Conservative mutation. From there, VAR_032718 has Isoleucine [I] at position 505 replaced with Threonine [T], a Missense Conservative mutation, and VAR_020706 with Asparagine [N] at position 60 replaced with Serine [S], a Missense Non-Conservative mutation..

[Audio] In this presentation, we will focus on the role of bioinformatics tools in predicting loss of function or pathogenic mutations. We will analyze the mutation suppressor homolog 2, commonly referred to as MSH2, which codes for a DNA repair proteins and acts as a tumor suppressor gene. MSH2 is comprised of 306763 base pairs and is located on chromosome 2, in the 21-16.3 position on the p arm. MSH2 plays an important role in Lynch Syndrome, both Type I and II..

[Audio] This slide focuses on five MSH2 mutations, where a given amino acid is replaced with another. These examples include alanine to threonine, histidine to glutamine, and more. All of these mutations are missense, meaning they alter the protein's structure and potentially its functionality. To better comprehend the impacts of these mutations, we must use bioinformatics tools..

[Audio] Cyclin Dependent Kinase Inhibitor 2A, or CDKN2A, is a 27572 bp protein located on chromosome 9, at location 21.3 on the p arm. It serves as a tumor suppressing gene, and regulates DNA damage response. It has been linked to diseases such as Melanoma, Lung Cancer, and Bladder Cancer. This slide provides an overview of CDKN2A and its related bioinformatics tools..

[Audio] This slide focuses on five mutations of the CDKN2A gene. Each case shows the replacement of one amino acid with another, leading to a missense non-conservative mutation. Four mutations are a result of a substitution, while the fifth is a conservative missense mutation. High likelihood exists that these mutations will cause a loss of function or a pathogenic effect..

[Audio] Bioinformatics tools can play an important role in predicting the possible effects of gene mutations. For example, mutations in BRCA1, BRCA2, MSH2, and CDKN2A can lead to a variety of conditions, such as breast cancer, pancreatic cancer and Lynch Syndrome. By using bioinformatics tools, it is possible to more accurately determine the effect or damaging outcome of any mutation..

[Audio] We are looking at the use of three predictor tools - SIFT, PolyPhen-2, and MutPred - which can be utilized to predict mutation effects and examine the similarities and differences between the tools. With these, we can analyze the impacts of various types of mutations, including loss of function, pathogenic and benign ones, and analyze the potential results for each mutation. By doing this, we can also draw comparisons between the tools..

[Audio] We'll be discussing several predictor tools used to predict the functionality of mutations. These tools are Polyphen-2, Mutation Assessor, Functional Impact of Protein Mutations, and Polymorphism Phenotyping v2. Understanding their use can give us an insight into the potential effects of mutations, whether they might lead to a loss of function or be pathogenic. Let's take a closer look at each of these tools..

[Audio] A table summarizing the pathogenic and benign mutations associated with BRCA1 is shown in this slide. The first column lists the name of the mutations, followed by their amino acid change and predicate in the next two columns. All the four mutations in the table are pathogenic, which means they can cause loss of function or lead to any pathogenic effect. Consequently, bioinformatics tools are essential in predicting the pathogenic or benign nature of mutations..

[Audio] Bioinformatic tools can be employed to assess the consequences of particular variants in order to predict their pathogenicity. This table exhibits examples of both pathogenic and benign variants related to [BRCA2] gene. Regarding each variant, the prediction capacity of two different tools, SIFT and PP-2, can be seen. As for the first example, the variant G25R has been forecasted to be pathogenic by both tools. It is possible to observe the same about the other examples, where F32L, Y42C and I505T have been predicted to be benign by both SIFT and PP-2. In conclusion, the variant N60S has been forecasted to be pathogenic by PP-2, although benign by SIFT..

[Audio] Slide 45 examines a table which evaluates the prediction abilities of a range of bioinformatics tools. It compares two variants to identify if they are pathogenic or benign. The results show VAR_054511 A2T is pathogenic, VAR_004470 H46Q is benign, VAR_042745 V102I is pathogenic according to PP-2 but benign according to SIFT, VAR_042751 L173P is pathogenic and VAR_019234 N127S is benign. This table offers insight into the efficacy of bioinformatics tools in predicting the pathogenic or benign outcomes of variants in the MSH2 gene..

[Audio] This slide shows a table containing data comparing pathogenic and benign mutations. The two types of mutations are contrasted based on their prediction ability. As an example, VAR_029287 is categorized as pathogenic while VAR_053033 is listed as benign. Both of these are assessed using the PP-2 and SIFT methods. Similarly, VAR_053034 is classified as pathogenic and VAR_053035 as benign, and they are both evaluated with the same methods. Lastly, RCV001246305 rs1820536864 T8S is identified as pathogenic..

[Audio] The graph shows that 5% of the mutation predictions had no effect on the gene's function, while the other 70% of predictions were pathogenic and had a detrimental effect on the gene's function..

[Audio] PolyPhen-2 and Mutational analysis are two bioinformatics tools that can be used for detecting loss of function or pathogenic mutations. They both offer efficient format of the data; however, the approach to data submission differs. For example, PolyPhen-2 requires data to be entered and results viewed one-by-one, while Mutational analysis enables submission of data as FASTA or accession # as well as the option of uploading the data..

[Audio] PolyPhen-2 and SIFT are two bioinformatics tools utilized for forecasting loss-of-function or pathogenic mutations. Both tools offer results in a shorter time period at 66% accuracy. SIFT and Mutation Assessor have a 100% success rate in delivering results. However, all the tools require the protein-to-protein mutation format before assessment with 66% accuracy in formatting. Despite the differences between the two tools in timing and format, similarities between them are not to be overlooked..

[Audio] Bioinformatics tools are powerful aides in the analysis of large amounts of biological data, capable of predicting the potential for a mutation to cause pathogenic effects. This presentation explored the accuracy of the outputs from three such tools: Mutation Assessor, SIFT and Polyphen-2 in predicting the likelihood of a mutation leading to a loss of function. It was found that Mutation Assessor failed to produce results for this instance, whilst SIFT and Polyphen-2 did, and were both accurate in their predictions at 100%. It is hoped this presentation has provided an understanding of how bioinformatics tools can be applied to examine genetic data and ascertain the potential of any pathogenic mutations. Thank you..