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© 2019 by the Krishnan Lab


Bioinformatics and Computational Biology

CMSE 491-890 | This course is an introduction to a variety of contemporary topics and questions in biology that can now be addressed using the combination of large-scale data and modern analytical techniques.

Using a series of recent papers, we will:

  1. Discuss major biological & biomedical topics,

  2. Explore big genomics & biomedical datasets, and

  3. Understand the underlying statistical, probabilistic, & machine-learning approaches.

Throughout the course, we will deliberate how to formulate problems, design computational projects, think critically about data & methods, communicate research findings, perform reproducible research, and practice open science.



Schedule: MWF 11:00am-12:10pm

Location: 351 Natural Sciences Building

Gaps, Missteps, and Errors in Statistical Data Analysis

This is a short (1-credit) intermediate-to-advanced course designed to:
1) Discuss common misunderstandings & typical errors in the practice of statistical data analysis.
2) Provide a mental toolkit for critical thinking and enquiry of analytical methods and results.
Classes will involve lectures, discussions, hands-on exercises, and homework about concepts critical to the day-to-day use and consumption of quantitative/computational techniques.

Topics include:

Underpowered statistics • Pseudoreplication • P-hacking & multiple hypothesis correction • Difference in significance & significant differences • Base rates & permutation tests • Regression to the mean • Descriptive statistics & spurious correlations • Estimation of error and uncertainty • (Others under consideration; Subject to small changes)

This is not an introductory course in statistics or programming. We will assume: 1) Familiarity with basic statistics & probability. 2) Ability to do basic data wrangling, analyses, & visualization using R or Python.
• Strongly recommended MSU courses: CMSE 201 and CMSE 890 Sec 301-or-304 and Sec 302.
• Contact Arjun for pointers to free online preparatory resources.

FALL 2018

Dates: Nov 5 – Dec 5 | Final student presentations: Dec 10 and 12 during regular class hours

Schedule: Mon/Wed 12:40-2:00 p.m.

202 Biochemistry Building

Modular Courses in Bioinformatics

We offer three modular bioinformatics courses each semester:

  1. CMSE 890 - Sec 301: Programming for Bioinformatics

  2. CMSE 890 - Sec 302: Statistical Analysis and Visualization

  3. CMSE 890 - Sec 303: Transcriptomic Data Analysis

These courses cover similar material to the Summer workshops, but will use the flipped-classroom format of having students watch video lectures online and come to class to apply the tools to real data. Each module is worth 1 credit.


FALL 2017

Schedule: 3:00 - 4:50pm, Tuesdays and Thursdays.

Location: A148A Plant & Soil Sciences Building.

Bioinformatics Workshops

Three week-long workshops:

  1. Week 1: Programming : intro & best practices

  2. Week 2: Data analysis: handy concepts & skills

  3. Week 3: Analysis of large-scale gene-expression data

These workshops provide training in Linux/R programming, statistics/visualization, and RNA-Seq. Cost is $50 per person per workshop and must be paid via an MSU grant. A limited number of fee waivers are available. Coffee and snacks are provided.



Schedule: 8:30am - 12:30pm, Monday through Friday.

Location: 1502/1503 Engineering Building.

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