Algorithmic Challenges in Genomics Boot Camp
2016-01-19..2016-01-22
Berkeley, United States
| title | author | length |
|---|---|---|
| Fundamentals of Stringology I: Recap on Exact Pattern Matching and Alignment Algorithms | Dan Gusfield | 01h 01m |
Presents two concrete algorithmic results and how they work: an exact‑matching method that computes prefix‑match lengths Z[i] in linear time via the… | ||
| Fundamentals of Stringology II: Probability and Statistics for Sequence Alignment | Mike Waterman | 52m |
Distinguishes exact versus non-overlapping vs overlapping pattern counts, shows non-overlap increases mean waiting time (example HTHTH gives probability P Q… | ||
| Fundamentals of Stringology III: Suffix Tress and Suffix Arrays | Dan Gusfield | 01h 01m |
Defines suffix trees and suffix arrays, shows exact claims and constructions: suffix trees admit linear-time construction and let pattern queries and… | ||
| Fundamentals of Stringology IV: The Burrows-Wheeler Transform | Dan Gusfield | 01h 07m |
Defines the LCP array (longest common prefix) and gives a linear-time two-finger skip trick to compute each H[k] by skipping H[k]−1 characters when possible… | ||
| Fundamental Algorithms in Deep Sequencing I: Assembly | Pavel Pevzner | 01h 05m |
Covers concrete algorithmic steps and measured results: SPAdes assembler handles extreme coverage variation and chimeric reads to produce nearly complete… | ||
| Fundamental Algorithms in Deep Sequencing II: Mapping/Alignment | Lior Pachter | 01h 20m |
Distinguishes classical sequence alignment (inferring evolutionary homology forests) from read mapping (short fragments with sequencing error), and argues most… | ||
| Fundamental Algorithms in Deep Sequencing III: Genomic Compression: Storage, Transmission, and ... | Noah M. Daniels | 01h 29m |
Genome data is compressible by exploiting redundancy: reference-free bucketed transforms like Scalce/mintS reorder reads for LZ77, reference-based encodings… | ||
| Introduction to Cancer Bioinformatics I: Inferring Genomic Variation from Tumor Sequencing Data | Ben Raphael, Niko Beerenwinkel | 01h 31m |
Course argues cancer sequencing needs beta‑binomial overdispersion models to avoid thousands of false SNV calls, shows ultra‑deep control detecting variants… | ||
| Introduction to Cancer Bioinformatics II: Tumor Phylogeny | Ben Raphael, Niko Beerenwinkel | 01h 03m |
Describes reconstructing tumor trees under the infinite sites assumption using perfect phylogeny, factoring sample variant allele frequencies (VAFs) via U×B… | ||
| Introduction to Cancer Bioinformatics III: Cancer Progression Networks | Niko Beerenwinkel | 56m |
Defines and fits explicit progression models: a restricted Bayesian network (oncogenetic tree) and its generalization (conjunctive Bayesian network) with one… | ||
| Introduction to Regulatory Genomics and Epigenomics I: Intro to the Biology of Gene Regulation | Nadav Ahituv | 01h 13m |
Regulatory sequences lack a simple code so mapping needs specific assays not broad claims: comparative genomics finds enhancers ~20–50% of the time, while p300… | ||
| Introduction to Regulatory Genomics and Epigenomics II: Motif Finding | Yaron Orenstein | 01h 20m |
Covers practical algorithms and data: MEME EM motif discovery uses an EM formulation to learn PWMs from unlabeled l‑mers; a simple k‑mer scoring method… | ||
| Introduction to Regulatory Genomics and Epigenomics III: Hidden Markov Models in Gene Regulation | Uwe Ohler | 01h 38m |
Uwe Ohler, MDC Berlin
Algorithmic Challenges in Genomics Boot Camp
https://simons.berkeley.edu/talks/uwe-ohler-2016-01-21 | ||
| Introduction to Biological Network Analysis I: Network Basics and Properties | Donna Slonim | 58m |
Graph representations map molecules or patients to nodes and physical, regulatory or co-expression ties to edges, and show that clustering coefficient… | ||
| Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to | Donna Slonim | 01h 07m |
Describes where protein‑protein interaction data come from (yeast two‑hybrid, co‑IP with mass spectrometry), catalogs public databases and Gene Ontology use… | ||
| Introduction to Biological Network Analysis III: Identifying Network Modules | Roded Sharan | 01h 06m |
Covers concrete algorithms and results for finding modules in protein networks: procedural heuristics like ENCODE, probabilistic scoring via NetworkBLAST… | ||
| Introduction to Biological Network Analysis IV: Network Alignment and Querying | Roded Sharan | 49m |
Argues that conserved subnetworks across species expose functional modules and improves specificity of interaction predictions, demonstrating tools like… | ||
| nothing matches | ||