Tentative topics & schedule
| Week | Date | Topics | Slides | Readings |
| Week 1 (Jan 7th-) | Mon | Review: Dynamic programming algorithm & Sequence alignment problems | slides1 and slides2 |
Paper 1: RNA-Seq: a revolutionary tool for transcriptomics Paper 2: The Technology and Biology of Single-Cell RNA Sequencing Paper 3: Single cell genomics: from phenomenology to mechanism |
| Wed | Background: RNA-seq and single-cell RNA-seq | link | ||
| Fri | No Lab for this week | |||
| Week 2 (Jan 14th-) |
Mon | Overview of I529 & Unsupervised learning: clustering algorithms | slides (handout) |
Molecular biology primer Genomic big data hitting the storage bottleneck Why so many clustering algorithms: a position paper The 5 Clustering Algorithms Data Scientists Need to Know |
| Wed | Clustering algorithms (single-cell RNA-seq) | link | ||
| Fri | Using R & clustering | |||
| Week 3 (Jan 21st-) |
Mon | MLK Jr. Day; NO class | Paper: MeShClust: an intelligent tool for clustering DNA sequences | |
| Wed | Clustering algorithms and beyond & clustering of biological sequences | clustering of biosequences; see scikit-learn documentation for evaluation | ||
| Fri | Lab: using R | |||
| Week 4 (Jan 28th-) |
Mon | Supervised learning: from linear regression to classification | notes; slides (handout) |
Paper: Machine learning in genetics and genomics A Review of Computational Methods for Finding Non-Coding RNA Genes |
| Wed | IU classes were closed due to the extreme weather | |||
| Fri | Lab: RNAseq data analysis (mapping & quantification) | |||
| Week 5 (Feb 4th-) |
Mon | Supervised learning: from linear regression to classification (cont.) | notes; slides (handout) |
Paper: Machine learning in genetics and genomics Review article on metagenomics: Metagenomics: Facts and Artifacts, and Computational Challenges |
| Wed | Naive Bayes classifier & taxonomic assignment problem in metagenome | slides (handout) | ||
| Fri | Lab: RNAseq data analysis (cont.) | |||
| Week 6 (Feb 11th-) |
Mon | Decision trees & random forests | slides (handout) |
Paper: What are decision trees? Paper: What is a support vector machine? |
| Wed | Decision trees & random forests (cont) Paper presentation on Orchid by group 2J |
slides (paper presentation) | ||
| Fri | Using scikit-learn | |||
| Week 7 (Feb 18th-) |
Mon | SVM and its applications in bioinformatics | notes |
Online book on ANN & deep learning Review: Deep learning for computational biology The human splicing code reveals new insights into the genetic determinants of disease Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (DeepBind) Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks Predicting effects of noncoding variants with deep learning–based sequence model (DeepSEA) |
| Wed | No class -- students attend this seminar (10-11am Luddy 3166) | paper | ||
| Fri | Lab: ML practice continued. | |||
| Week 8 (Feb 25th-) |
Mon | Paper presentation by JP group (peptide presentation prediction) |
slides (paper presentation) |
DeepQA for protein model evaluation deepTarget for miRNA target prediction slides (RF vs deep learning) Review: Machine learning applications in genetics and genomics |
| Wed | ANN: overview & backpropagation algorithm |
slides (handout) Visualization of ANN a video for fun |
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| Fri | Lab: ML practice | |||
| Week 9 (Mar 4th-) |
Mon | CNN: convolutional neural network | CNN: slides (handout) |
DeepCfp1 (paper) DeepCRISPR |
| Wed | Paper presentation by group M+ (semisoft clustering) DNN (deep neural network) |
paper presentation: slides DNN: slides (handout) |
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| Fri | ||||
| Week 10 (Mar 11th-) |
Spring break; no class | |||
| Week 11 (Mar 18th-) |
Mon | Probabilistic modeling & profiles, Markov chains |
slides (handout) slides (handout) slides (handout) watch/hear this |
BSA Chapter 5 Protein families Some old slides on gene finding |
| Wed | Hidden Markov models & Generalized HMM | slides (handout) | ||
| Fri | Lab: Learning & Prediction (using Glimmer) | |||
| Week 12 (Mar 25th-) |
Mon | Hidden Markov models & Generalized HMM | slides (handout) |
A presentation on epigenomics Paper: A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective Paper: Identifying and mitigating bias in next-generation sequencing methods for chromatin biology |
| Wed | HMM parameter estimation & applications in epigenomics |
slides (parameter est.) (handout) slides (epigenomics) (handout) A short video on epigenetics) |
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| Fri | Lab: Using ChromHMM | |||
| Week 13 (Apr 1st-) |
Mon | Profile HMM & Phylo-HMM |
Profile HMM: slides (handout) |
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| Wed | Paper presentation by QT group on deep generative model for single-cell transcriptomics | |||
| Fri | Lab: Using hmmer | link | ||
| Week 14 (Apr 8th-) |
Mon | Phylo-HMM & Multivariate HMM | Phylo-HMM: slides (handout) |
Primer 10 & 11 & 12 nice slides from Michael Schatz |
| Wed | EM algorithm & motif finding |
slides (handout) motif in (music) MEME |
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| Fri | Lab: hw & project discusssion | |||
| Week 15 (Apr 15th-) |
Mon | Bayesian netowrk & Module network |
(BN) slides (handout) (Module network) slides (handout) |
Primer 5 & 6 |
| Wed | Review of bioinformatics problems/ML approaches | slides (handout) | ||
| Fri | No lab | |||
| Week 16 (Apr 22nd-) |
Mon | Project presentation (group2J, JP) | ||
| Wed | Project presentation (M+, QT) | |||
| Fri | No lab | |||