NextGen Sequence Analysis
This hands-on training will equip participants with knowledge and skills on how to generate, manipulate, and analyze whole genome, whole exome, and targeted NGS data and discover germline SNPs, indels, SVs, and CNVs; somatic SNVs, SVs, and indels, and RNASeq SNPs and indels. The objective is to enhance the researchers‘ capacity to conduct world class scientific research and boost their institution‘s profile as a leading diagnostic and scientific centre.
The training is ideal for graduates in biological, medical, or agricultural sciences who are using or intend to use nextgen sequencing in their work. The training will teach you how to:
- Learn the science, methods and applications of NGS.
- Discover SNPs, SNVs, SVs, indels and CNVs from large genetic datasets
- Identify varinats of clinical significance and diagnose medical conditions.
- Optimize therapy and precision medicine
Gel Electrophoresis Boot Camp
2-dimensional gel electrophoresis (2-DE) is a powerful tool in proteomics and software-based analysis of gel images is an important step in the process. Various computer-based methods have been proposed for the detection of protein spots in 2-DE images (Brauner, 2014).
This practical training is aimed at students and researchers who are applying or planning to use gel electrophoresis in their research. The aim of this training is to equip the participants with practical skills on how to separate proteins using different gel electrophoresis techniques and how to visualize, explore, analyse, assemble, and submit 2D gel data using bioinformatics tools.
This training will teach you how to:
- Identify components of the gel electrophoresis system
- Prepare reagent and stock solutions for SDS-PAGE (Laemmli) Buffer System and discontinuous Native PAGE (Ornstein-Davis) and continuous Native PAGE systems
- Set up and operate the gel electrophoresis system
- Visualize, explore, analyse, assemble, and submit 2D gel data using bioinformatics tools.
- Maintain the gel electrophoresis system
PCR Boot Camp
Polymerase chain reaction (PCR) has been hailed as one of the most important advances in molecular biology and is an indispensable tool in biomedical research. The objective of this intensive 5-day hands-on training is to impart transferrable skills on planning, setting-up, running and analysing PCR experiments.
Participants will be taken through principles of DNA replication, types and variants of PCR, and PCR principles and workflows. They will learn how to design and synthesize primers, prepare samples, design PCR assays, set up software, and apply PCR in DNA cloning for sequencing, functional gene analysis, disease diagnosis, paternity testing, and forensics. PCR Optimization and validation will also be taught. The course is right for any person who has an interest in starting to use PCR or current new users who want further training on Gene Expression Applications.
Perl Programming for Biologists
Perl is a powerful programming tool widely used in the analysis of biological data. It not only combines the modern robustness of Java with the expedient pragmatism of scripting languages but also offers both the low-level system access of C and the high-level elegance of Lisp. Perl is widely used in bioinformatics due to its powerful text-processing capacity. It allows the easy implementation of NLP and bio-informatics algorithms and the extraction and generation of textual data.
Perl is also easy to learn, is portable and multiplatform, has a large library of extensions, is component-oriented, is good in prototyping, and is efficient in the slicing, dicing, twisting, wringing, smoothing, summarizing and otherwise mangling of text. The objective of this course is to equip biologists who have little or no programming experience with the requisite skills.
This training will teach you how to:
- Install the Perl interpreter
- Manipulate files and directories
- Use arrays and array functions to solve a variety of problems
- Use the powerful regular expression capabilities of Perl.
- Generate reports, use hashes to solve biological problems, and write programs that solve common biological problems...
Microarray Data Analysis
Microarrays have found wide use in drug discovery and development, diagnostics, pharmacogenomics, toxicology, forensics, cladistics, bio defence, biomarker discovery and development, and gene/disease association studies. Analysis of the data produced by microarrays is thus of great importance. In this course, students are taken through an intensive, hands-on training on creating microarrays and analyzing and interpreting microarray data.
The training includes introduction to the R programming language and the use of various Bioconductor packages to load raw intensity files, analyze the files, retrieve sample annotation, assess data quality using images, box plots, and quality measures, normalize the data, and identify differentially expressed (DE) genes for 2-group and multiple arrays. Students are also introduced to NCBI’s Gene Expression Omnibus (GEO) and EMBL’s ArrayExpress. Normalization tools covered include the multi-array average (RMA) algorithm, MAS 5.0, Plier, dChip, and GCRMA.
The A-Z of Biological Databases
The student is introduced to biological databases including nucleotide sequence, protein sequence, protein structure, protein model, protein-protein interaction, RNA, and carbohydrate structure databases. Other databases covered include signal transduction pathway, metabolic pathway, microarray, genome, exosomal, and taxonomic databases. Web services including REST,SOAP,BioMart and BioMart Web Services are also covered. Participants also learn how to design and deploy mysql databases and receive training on the following:
- Writing html pages and CSS code
- Generating dynamic content on Web pages and Web applications using PHP
- Connecting to MySQL databases using Perl/CGI and PHP
Bioinformatics 1
Bioinformatics is a discipline that involves the use of computer science and biology to solve biomedical or biological problems. It utilizes large amounts of data and complicated datasets to make deductions which are useful in solving medical or biological problems. Through bioinformatics, the aetiology of diseases can be investigated at gene level and understood hence making the discipline a vital tool in medicine. In this course, the student is introduced to the concepts of Bioinformatics. Hands-on sessions will familiarize students with the details and use of common tools and resources. The course will cover the use of NCBI's Entrez and EBI's SRS, EMBL, GenBank, DDBJ and other biological databases, file formats, BLAST, PSI-BLAST, ClustalW, phylogenetic analysis, protein analysis, Pfam, PRINTS, BLOCKS, Prosite and the PDB and MMDB. Principles of drug design (both structure-based and ligand-based) will be covered in depth and topics in whole transcriptome shotgun sequencing (RNA SEQ), transcriptomics, systems biology, and metabolomics will also be covered. An introduction to database design and the principles of programming languages and HTML/CSS scripting and database design using MySQL will also be provided.
Hands-on sessions will familiarize students with the details and use of common tools and resources. The objective is to enhance the researchers’ capacity to conduct world class scientific research and boost their institution’s profile as a leading diagnostic and scientific centre.
Metabolomics Training
Metabolomics is the fastest growing area of life sciences and involves the systematic study of the small molecular metabolites in a cell, tissue, biofluid or cell culture media that are the tangible result of cellular processes or responses to an environmental stress. The main goal of metabolomics is to identify a list of differentially regulated metabolites for use in biomarker discovery, pathway analysis, model construction, and scientific literature.
The objective of this course is to acquaint the learner with emerging trends in metabolomics as well as equip him with skills for generating and interrogating metabolomic data. The course is ideal for students and researchers working in the pharma, biotech, food, and agricultural industries. In this course, students receive hands-on trainng on:
- Experimental design of metabolomics studies
- Sample preparation including extraction, chromatography, and ionization efficiency maximization
- Generation of raw metabolomic LC/MS or GC/MS data
- Pre-processing data analysis including conversion of raw data to the mXMZL format, feature detection, retention time correction and statistical analysis
- Metabolite identification
- Hypothesis testing
- Experimental validation
Advanced Drug Design with HPC
The advent of high performance cloud (HPC) computing, genomics, proteomics, bioinformatics and efficient technologies like virtual screening, in silico screening, Molecular Docking, ADMET screening and Structure-based drug design have revolutionized the way in which drugs are developed. In this training, partcipants will learn the science involved in disease target identification, virtual screening techniques, methods used in in-silico generation of ligands, protein optimization & energy minimization, molecular Docking, creation of Grid Paramater & Dock Parameter files, and running the Docking Algorithm.
They will also learn how to select potent inhibitors on the basis of binding energies and Lipinski’s Rule of 5, how to look for H-bond between ligand and active site of the residue of protein as well as aspects of drug Likeness,ADME & Toxicity. Practical hands-on training will encompass training on the use of AWS EC2, software such as MarvinSketch, UCSF Chimera, AutoDock Tools, Open Babel, and SPDV among others.
In Silico Vaccine Design | Kenya Institute of Bioinformatics
This 2-day course will cover the state-of-the-art methods for in-silico T- and B cell epitope discovery. We will touch upon different prediction tools and database searches. Publicly available web servers such as The Vaccine Investigation and Online Information Network (VIOLIN; http://www.violinet.org ), Vaccine Page and open-source computational software will be used. A few review papers on prediction servers and methods for epitope identification will be presented.
Aspects of reverse vaccinology including case studies will be presented. No advanced computer skills are needed. Even though we assume a basic knowledge of the major functions of the immune system, important parts will be briefly explained. Training can be done in-house or at a different training location.
Herbal Medicine Optimization using In Silico Methods
This training seeks to use genomics, proteomics, bioinformatics and efficient technologies like virtual screening, in silico screening, Molecular Docking, ADMET screening and Structure-based drug design to validate and optimize chemical compounds found in herbs. In this training, partcipants will learn the science involved in characterization of herbal chemical compounds, disease target identification, virtual screening techniques, methods used in in-silico generation of ligands, protein optimization & energy minimization, molecular Docking, creation of Grid Paramater & Dock Parameter files, and running the Docking Algorithm.
They will also learn how to select potent inhibitors on the basis of binding energies and Lipinski’s Rule of 5, how to look for H-bond between ligand and active site of the residue of protein as well as aspects of drug Likeness,ADME & Toxicity. Practical hands-on training will encompass training on the use of software such as MarvinSketch, UCSF Chimera, AutoDock Tools, Open Babel, and SPDV among others.
Practical Methods in Preclinical Drug Testing
There has been an explosion of alternative methods to test the safety and efficacy of drugs. Newer techniques include in silico ADMET screening, organ-on-chips technology, 3D multicell bioprinting, microfluidic chip platforms, and Meta-Chips among others. Some of these technologies are still at the experimental stages. This course seeks to equip students with knowledge on the current state of the art in preclinical drug testing. The course also seeks to equip them with skills in traditional preclinical drug testing methods.
Students will learn how to perform analytical and bioanalytical tests, wet lab and in silico ADMET tests, and repeat-dose toxicology studies.
Biolinux Boot Camp
BioLinux is an extremely useful operating system in bioinformatics. Its advantages are that it is free since it is developed using open source tools, can be installed on anything from a laptop to a large server or run as a virtual machine, and has more than 250 bioinformatics packages, 50 graphical applications, and hundreds of command line tools. It can also be run live from a DVD or USB flash disk without installing and has found wide use in virtualization.
This training will teach you how to:
- Access the Linux environment on a Windows machine via JVM
- Perform Remote computing using SSH and Putty
- Master Linux commands
- Manipulate files and directories in Linux
- Transfer files from your local machine to remote servers and back.
- Perform bioinformatics tasks in Linux commandline. ...
Whole Transcriptome Shotgun Sequencing (WTSS) Data Analysis
Whereas microarrays are important in the identification and quantification of the mRNA transcripts, they are encumbered by the inability to identify novel transcripts, limited dynamic range for detection, and difficulty in replicability and inter-experimental comparison. RNA Seq, also known as whole transcriptome shotgun sequencing (WTSS) overcomes many of these problems. Making use of high-throughput next-generation sequencing methods, sequencing the entire transcriptome permits both transcript discovery and robust digital quantitative analysis of gene expression levels.
To achieve its two major functions (transcript discovery and gene expression quantification), WTSS relies on the generation of short reads of transcript sequence information. Its advantages include high-throughput sequencing, lack of bacterial cloning constraints, high coverage, ability to discover new exons and handle RNA splicing, and low cost. This course equips students with the following skills:
- Poly (A)-selection of RNA
- Fragmentation of RNA to an average length
- Conversion into cDNA
- Sequencing.
- Mapping of the reads onto the genome
- Calculation of the transcript prevalence
