bioinformatics and artificial intelligence .pptx

The development of software tools that help to store, manipulate, and analyze biological data and more.
The goal of bioinformatics aims to predict the biological processes in health and disease.

To do this it needs to combine statistics, biology, and computer science which create new areas such as biostatics, data science, and computational biology to solve bioinformatics issues.

1. 2 Bioinformatics : Basics & ai What is Bioinformatics The development of software tools that help to store, manipulate, and analyze biological data and more. The goal of bioinformatics aims to predict the biological processes in health and disease. To do this it needs to combine statistics, biology, and computer science which create new areas such as biostatics, data science, and computational biology to solve bioinformatics issues.

2. Bioinformatics relationships with OMICS Bioinformatics can be applied to various areas of life sciences ,such as: genomics (the study of DNA) transcriptomics (the study of expressed genetic information) proteomics (the study of proteins) the basic principle in these different approaches is quite similar: starting from complex raw data and applying a series of commands to process them (cleaning, sequence alignment, taxonomic assignment, etc.) thus, enabling conclusions to be drawn.

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4. 2 Using Bioinformatics for Gene Analysis, Taxonomy & Evolution BIOINFORMATICS: Basics & AI

5. Gene analysis using bioinformatics tools During the past several years, bioinformatics enrichment tools have played a significant and successful role in contributing to the gene functional analysis of large gene lists for various high-throughput biological studies The principal foundation of enrichment analysis is that if a biological process is abnormal in a given study, the co-functioning genes should have a higher (enriched) potential to be selected as a relevant group by the high throughput screening technologies The tools systematically map a large number of interesting genes in a list to the associated biological annotation terms, and then statistically examine the enrichment of gene members for each of the annotation terms by comparing the outcome to the control BIOINFORMATICS: Basics & AI

6. DGE analysis is a technique used in molecular biology to compare gene expression levels between two or more sample groups (healthy vs disease tissues or cells exposed to different treatments ) DGE analysis’s primary objective is the identification of genes differentially expressed in settings being compared This tool can help in identifying genes involved in a particular biological process, disease, or response to treatment (providing information on gene regulation and underlying biological mechanisms ) This multiple-step analysis is frequently used in studies of disease, where it can help in the identification of biomarkers for diagnosis and prognosis or evaluate the effectiveness of specific treatment Differential gene expression BIOINFORMATICS: Basics & AI

7. Differential gene expression analysis steps Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review 2024 by Diletta Rosati et al. BIOINFORMATICS: Basics & AI

8. What is taxonomy? A taxonomy is a classification scheme The oldest form of taxonomy the Linnean classification of organisms is a very recognized pattern Typically, taxonomies are hierarchical BIOINFORMATICS: Basics & AI

9. For example : Virus taxonomy is a virology subspecialty that addresses the grouping (classification) of viruses (physical entities) into categories (concepts) called taxa and the development and implementation of a standardized system of naming (nomenclature) for taxa. virology, with many more viruses now known solely from sequence data than have been characterized experimentally(the family  Genomoviridae  currently comprises a single classified virus, whereas more than 120 possible members have been sequenced from diverse environments). The current computational approaches assisting virus taxonomy may be based on: (1) patterns of nucleotide or amino acid sequence variation, (2) gene or protein content by homology, (3) phylogeny, and (4) pair-wise genetic distance How does taxonomy use bioinformatics? BIOINFORMATICS: Basics & AI Bioinformatics of virus taxonomy: foundations and tools for developing sequence-based hierarchical classification 2022 by Alexander E Gorbalenya et al.

10. BIOINFORMATICS: Basics & AI Evolution & the role of Bioinformatics Evolutionary biology bioinformatics is an interdisciplinary field that combines evolutionary biology and bioinformatics. It is an important tool for analyzing and interpreting large sets of biological data for understanding the role of evolution in the development of life. Evolutionary biology bioinformatics applications

11. 2 Using Bioinformatics for Biological Pathways & Networks

12. BIOINFORMATICS: Basics & AI Biological systems are often represented as networks which are complex sets of binary interactions or relations between different entities Network analysis seeks to understand the relationships within biological networks such as metabolic or protein-protein interaction networks Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography and protein Nuclear  Magnetic Resonance  Spectroscopy (protein NMR) and a central question in structural  bioinformatics  is whether it is practical to predict possible protein-protein interactions only based on these 3D shapes, without performing protein-protein interaction experiments Biological pathways /networks and the use of bioinformatics

13. BIOINFORMATICS: Basics & AI The protein network of Human P53 protein : The p53 protein, encoded by the TP53 gene in humans, is commonly referred to as the “guardian of the genome” due to its activities directed at maintaining genomic stability through the repair of damaged DNA and the integration of cell birth/death signaling pathways with DNA damage checkpoints

14. BIOINFORMATICS: Basics & AI The STRING: Search Tool for the Retrieval of Interacting Genes/Proteins   STRING is the knowledgebase and software tool for  known and predicted protein-protein interactions . It includes direct (physical) and indirect (functional) associations derived from various sources, such as genomic context, high-throughput experiments, (conserved) co-expression, and the literature

15. 2 Using Bioinformatics for Forensic Analysis

16. BIOINFORMATICS: Basics & AI What is forensic analysis and why is it so important? Forensic analysis definition can be described as a detailed process of detecting, investigating, and documenting the reason, course, and consequences of a security incident or violation against state and organization laws. Forensic analysis is often used for providing evidence in court hearings, especially in criminal investigations. It employs wide range of investigative procedures and technologies.

17. BIOINFORMATICS: Basics & AI What is the relationship between Forensic Science and Bioinformatics? Applying bioinformatics in forensics means applying biological science & computational knowledge for investigation or investigative research. Genetic testing results are integrated with information collected by multidisciplinary teams composed of medical examiners, forensic pathologists, anthropologists, forensic dentists, fingerprint specialists, radiologists, and experts in the search and recovery of physical evidence

18. BIOINFORMATICS: Basics & AI The use of Genetics in Forensic Science

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20. BIOINFORMATICS: Basics & AI The use of PCR allows to analyze DNA from samples as small as a single cell and, therefore, DNA typing analysis using STRs can be performed on a large variety of materials, such as cigarette ends, skeletal remains, urine, tissues on a gun muzzle and on bullets, dismembered and decayed body parts At present, the most discriminative power in DNA identification is obtained by matching 13–17 of nuclear STR markers of a victim’s profile (personal items, like toothbrushes and used shavers) to a direct antemortem sample of the victim or to family references: either or both biological parents of the victim. Forensic DNA and bioinformatics 2007, Lucia Bianchi and Pietro Lio

21. BIOINFORMATICS: Basics & AI One of the most contentious issues in forensic use of DNA evidence is how to estimate the probability that two DNA profiles match by chance . Given allele frequencies, DNA profiles are screened automatically for matches between profiles of person to person(s), person to scene(s) and scene to scene(s). Although the most used set of STR loci are spread on all the chromosomes (some chromosomes have just one), they have a different mutation rate STR-Databases ENFSI DNA WG STR Mass Disaster Kinship Analysis Program (MDKAP) DNA Profiling D atabase in EU((STADNAP)) The European Network of Forensic Science Institutes(ENFSI) Bioinformatics software and databases used in forensic science Mass Fatality Identification System (M- FYSis )

22. 2 Using Bioinformatics for Gene Therapy

23. BIOINFORMATICS: Basics & AI What Is Gene Therapy? Gene delivery is the method of transferring foreign genes to host cells for purposes such as genetic research or gene therapy Gene therapy approaches : gene addition and gene editing. There are two types of gene editing: gene silencing and gene correction.

24. BIOINFORMATICS: Basics & AI Mathematical and Computational Modeling of Gene Therapy While the goal of gene therapy is the expression of the protein encoded by the delivered gene, it is not sufficient that the gene product is expressed. The downstream impact of this protein, expressed for a sufficient duration and magnitude, must exert a beneficial effect on the protein network, whether this “effect” is short or long-term Currently, gene expression studies often examine changes in target protein concentration over a period of time as opposed to the effects of these changes in protein expression on the overall pathways involved The goal of systems biology is to overcome this by incorporating information on the expressed protein and the pathways in which it is involved, allowing us to optimize gene therapy approaches This includes systematic use of mathematical models and in silico simulations of gene therapy

25. BIOINFORMATICS: Basics & AI By leveraging bioinformatics, researchers can identify target sites for gene editing, predict and minimize off-target effects, enhance the efficiency of CRISPR systems, understand the functional consequences of genetic alterations, explore evolutionary relationships, and gain comprehensive insights into biological systems. comparative genomics and homology analysis are discussed as vital approaches that leverage bioinformatics to understand evolutionary relationships and identify conserved elements across species. Integration of multi-omics data, such as genomics, transcriptomics, and proteomics, is highlighted as a powerful strategy for gaining comprehensive insights into biological systems The role of bioinformatics in gene editing

26. CRISPR /Cas history In E.Coli CRISPR history:discovery,characterization,and prosperity by Wenyuan Han et al 2017 BIOINFORMATICS: Basics & AI

27. What is CRISPR and where does it come from originally? In 1987 microbiologists found that Archaea and bacteria have evolved adaptive immune defenses against foreign nucleic acids from cellular invaders CRISPR : C lustered R egularly I nterspaced S hort P alindromic R epeats Cas : C RISPR- a ssociated s ystems BIOINFORMATICS: Basics & AI

28. CRISPR system BIOINFORMATICS: Basics & AI

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30. Adeno-associated Virus (AAV ) Lentivirus Adenovirus Delivering CRISPR: a review of the challenges and approaches by A.Lino et al 2018 BIOINFORMATICS: Basics & AI

31. Target DNA site selection and sgRNA design CRISPR/Cas system is able to target any 23 bp sequence that contains PAM motif on the either strand of DNA. SpCas9 PAM occur every 8 bps Directed evolution and structure-guided rational design has allowed for engineering of Cas9 variants with altered PAM sequence specificity These are not easy tasks Maybe this system is less specific due to shorter targeting sequence Transformed DNA are then amplified by PCR and purified by agarose gel electrophoresis ,DNA sequencing-gal assay, Western blotting and co-purification BIOINFORMATICS: Basics & AI

32. Websites to design sgRNA Chopchop CRISPR=http://chopchop.cbu.uib.no/ Crispor=http://crispor.tefor.net/ E-crispr design=http://www.e-crisp.org/E-CRISP/ clontech crispr=www.takarabio.com/ Crispr target finder=http://flycrispr.molbio.wisc.edu/tools BIOINFORMATICS: Basics & AI

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34. 2 Using Bioinformatics for Unravel Biological Data

35. BIOINFORMATICS: Basics & AI One of the most studied complex systems is the cell. However, its functioning is still largely unknown. It comprises diverse molecular structures, forming complex, dynamical molecular machinery, which can be naturally represented as a system of various types of interconnected molecular and functional networks. Recent technological advances in high-throughput biology have generated vast amounts of disparate biological data describing different aspects of cellular functioning also known as omics layers Biological Data and OMICS layers

36. BIOINFORMATICS: Basics & AI Gene databanks

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38. BIOINFORMATICS: Basics & AI Protein databanks

39. BIOINFORMATICS: Basics & AI Metabolomics database

40. 2 Using Bioinformatics for Drug & Vaccine Design

41. BIOINFORMATICS: Basics & AI What are vaccines?

42. BIOINFORMATICS: Basics & AI Reverse vaccinology Reverse vaccinology is a methodology that uses bioinformatics tools for the identification of structures from bacteria, viruses, parasites, cancer cells, or allergens that could induce an immune response capable of protecting against a specific disease. 1 2 3 4 5

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44. 2 Using Bioinformatics for Producing High Productivity Crops

45. Bioinformatics and Crop Improvement: A Green Revolution in the Making BIOINFORMATICS: Basics & AI The growing knowledge about molecules and mechanisms linked to specific phenotypic traits and responses to biotic or abiotic stresses, coupled with the predictive capabilities of bioinformatics, is influencing agricultural practices. This, in turn, encourages innovative approaches in diagnostics, monitoring, and traceability, ultimately enhancing human benefits at reduced costs and supporting sustainability . Genomic Insights for Crop Enhancement Functional Annotation and Prediction Comparative Genomics Marker-Assisted Breeding Transcriptomics and expression profiling Metabolomics and pathway analysis Phylogenic crop evolution Prominent bioinformatics tools include the BLAST program for homology searching, GENSCAN, GENIE for gene-finding, SAPS for statistical analysis of protein sequences, CLUSTAL, ITERALIGN for multiple sequence alignment, r-SCAN STATISTICS for target array clustering, and over-dispersion analysis.

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47. Applying Machine Learning to Crop Breeding BIOINFORMATICS: Basics & AI Machine learning (ML) allows algorithms to interpret data by learning patterns through experience. For large, diverse and formless datasets, such as those generated by photo imaging or sequencing, ML can provide substantial advantages over other analytical approaches . With the aid of ML, crop breeders can efficiently phenotype plants and mine diverse datasets for patterns such as associations between DNA sequences and traits. Machine learning has two major category : High Throughput Crop Phenotyping Machine Learning in Crop Genomics Research