This course will introduce students to bioinformatic analysis of next generation sequencing data, particularly for DNA-seq, RNA-seq, CHIP-seq, and epigenomics. The course will be comprised of lectures and hand-on sessions. Lectures will cover background knowledge and survey various software programs. For hand-on sessions, command line tools will be presented and the galaxy web based platform will be used to analyze primary data. Cloud computing, genomic databases, and de novo assembly will be surveyed.
Overview on Bioinformatics Tools: Galaxy (Pre-processing, Format Conversion, etc.), Databases and Tools, SNP Callers, Cloud; RNA-seq; CHIP-seq; Epigenomics; Cloud Computing; 10k Genomes; Data Visualization; Comparative Genomics and Genome Alignment (biology + software); Tutorial & Laboratory for de novo Assembly; RNA-seq, DNA-sequencing
Note: Students will need to bring their own laptop to this program
The logical next step after genome sequencing or proteomics analyses–and a necessary prequel to biomarker and drug target discovery–is to identify proteins of interest and their activities.
This course will combine lectures with computer labs to provide an introduction to the bioinformatics resources and methodologies commonly used for protein analyses. Course participants are expected to know the basic biology of proteins, and will leave with the ability to perform detailed analyses of protein sequences and structures.
Database Resources & Text Searching: where and how to look; Theory and Application of Sequence Searches: BLAST, Multiple Sequence Alignment, and Peptide/Pattern Match; Protein Classification: background and how to construct protein families; Structure-based Analyses: principles and tools, and how to do structure alignments and predict functional sites; Protein Function Prediction: tour of how to do it and how to avoid pitfalls; Biomedical Ontologies: introduction and uses; Functional Interpretation of High-throughput Data: function and Pathway, analysis for proteomic and genomic data
RNA-seq or RNA sequencing is a new technology that utilizes the latest in next-generation sequencing approaches to obtain information about the presence/absence as well as the quantity of transcribed RNA (mRNA, rRNA, tRNA, or miRNA). Soon RNA-seq will be transplanting microarrays as the go-to procedure for analyzing the transcriptome of any genome. In this workshop, we will provide hands-on experience with RNA-seq - from the bench to the post-sequencing data acquisition (Illumina NextSeq) and analysis using the latest bioinformatics approaches. With a team of researchers from the NIH, area academic institutions and Illumina, we will cover examples of methodological approaches and applications of RNA-seq analysis to a variety of basic science and clinical biomedical research problems.
An Introduction to NGS and RNA-seq; Basics of RNA Sequencing and Analysis; Introduction to Downstream Analysis; RNA-seq Gene Expression Data Analysis Pipeline: Methods, Tools and Issues; Efficient Library Preparation from Embryonic Stem Cells (ESCs); Transcriptomic Changes in Human Brain Development; Bioconductor and RNA-seq Data Analysis; Integrating Gene Expression and Pathway Analysis in Developing Early Disease Biomarkers: A Genomic Approach; RNA-seq and CHIP-seq with Galaxy; Basic Downstream Analysis of RNA-seq and CHIP-seq Data with DAVID and IPA; Metaseq: A Python Package for Integrative Genome-wide Analysis; RNa seq Data Analysis in the Context of Biological Networks; RNA-seq of the Small RNAs of the Nucleolus; Transcriptome Profiling of CTLs using RNA-seq.
Processing of RNA libraries; Load Sequencing Reactions; Recovery of Sequencing Data; Using BaseSpace to Analysis RNA-seq Data; Introduction to Linux, Sed, Awk and Bash Scripting; RNA-seq Analysis with the Tuxedo Package- Command Line
The emerging field of Clinical Proteomics refers to the application of proteomic technologies to investigate protein expression differences in clinically obtained biological samples. A consequence of these approaches has been the renewed interest in identifying new biomarkers which may useful for the diagnosis of disease or monitoring of patient response to therapy. Researchers involved in Clinical Proteomics face numerous methodological, analytical, and statistical challenges that need to be addressed in order for successful completion of projects. Additionally, large scale biomarker discovery efforts include the coordination of many different disciplines, such as biochemistry, proteomics and mass spectrometry, clinical chemistry and bioinformatics. In this course, students will be exposed to many of the challenging aspects of biomarker discovery projects, as well as to the numerous analytical platforms that may be employed.
Study Design and Sample Collection/Storage of Clinical Samples; Sample Handling and Preparation (Chromatographic methods to reduce the complexity of the sample, such as ion-exchange chromatography, immunoaffinity depletion); Quality Control and Reproducibility in Measurements; Analytical Tools for the measurement of quantitative difference between clinical samples: 2D gel electrophoresis, including DIGE; MALDI/SELDI-TOF-MS for Serum Protein Profiling, Multiplex Protein/Cytokine Arrays, Differential isotopic labeling, such as ICAT, SILAC Quantitative Data Analysis of 2D and Mass Spec Data Sets; Multivariate Statistical Models; MS Protein Patters vs. Protein Panels for Diagnostic Determination