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Introduction to Data Driven Life Sciences | Spring 2025

Syllabus

https://xkcd.com/2054/

Place and Time

Huck Life Sciences Bldg 005 | Tuesday, Thursday 10:35am - 11:50am EST

Tip

The class is in person only. However, for those who are located in Hershey or unable to attend a particular lecture1 a Zoom link is provided.

1Notify the instructor in advance if you are unable to attend a lecture for whatever reason.

Instructor

Anton Nekrutenko aun1@psu.edu Wartik 505 Office hours by appointment only

Note

When contacting instructor use the above e-mail and include "BMMB554" in the subject line (simply click on e-mail address. It will invoke an email client with subject line pre-filled). A preferred way of contacting me is using MS teams via my PSU account aun1@psu.edu

Course logistics

This course does not use Canvas. Canvas is a convoluted system with too many features and undefined purpose. Instead, this course is served from GitHub.

Caution

Do not contact me through Canvas! I will not check my Inbox there. Instead, contact me via email as described above.

Grading and quizzes

Important

Each Tuesday class will start with a 10 min quiz. The quiz will be based on reading assignments from the previous week. Each quiz will be scored on [0;100] scale. Aggregate of quiz scores will represent 33.3% of the final grade.

Attendance (33.3%) + Quizzes (33.3%) + Final Project (33.3%) ≈ 100%


A note on notebook environments

We will use JupyterLab as our main platform. JupyterLab is an example of a "notebook environment." There are several frameworks for interactive data exploration using code snippets. These include:

JupyterLab set up for Shell, Python, and Git lectures

  1. Start JupyterLab
  2. Start terminal within JupyterLab instance

Adding yourself to a Galaxy queue for this class

  1. Go to https://usegalaxy.org and log in
  2. Click on this link -> https://usegalaxy.org/join-training/bmmb554-25/
  3. You are all set

Lectures

Links to individual lectures will be posted below.


Block 1: Shell / Python / Generative-AI

Lecture Date Topic Reading before lecture Quiz
1 Jan 14 Introduction and History
2 Jan 16 History of sequencing and simulation of sequencing process: thinking conceptually. Shell I Y
3 Jan 23 Simulation of sequencing process: implementing it. Shell II Y
4/5 Jan 28/30 Python 1 - Variables, expressions, statements, functions Chapters 1, 2, 3, 8 and 10 from "Think Python" Y
6 Feb 4 Python 2 - Strings and lists and FASTQ N
7 Feb 6 Python 3 - A more careful look at lists and dictionaries N
8 Feb 11 Python 4 - Processing files N
9 Feb 13 Pandas 1 - Creating and using dataframes Home assignment
10 Feb 18 Pandas 2 - Narrow data versus wide data. Melts, pivots, and joins
11 Feb 20 Creating websites on GitHub infrastrcture Home assignment due
12/13 Feb 27 Project description (genes)/ Forming project groups N
14 Mar 4 Galaxy / Datasources / Genome Browsers
14 Mar 6 Project description / Genome Browsers
16 Mar 18 Illumina sequencing / Understanding dataset collections for complex analyses
17 Mar 18 Workflows: GWL, Nextflow, Snakemake, and WDL
18 Mar 20 Using conditionals and advanced logic in Galaxy workflows
19 Mar 24 Executing Galaxy workflows programmatically
20 Mar 26 Creating Galaxy tools: BioConda, BioContainers, and Planemo
21 Apr 1 Interactive environments: Why we learned Python in the beginning
22 Apr 3 Mappers and aligners: What's the difference 1
23 Apr 8 Mappers and aligners: What the difference 2
24 Apr 10 Common analyses 1: Resequencing
25 Apr 15 Common analyses 2: Epigenetics
26 Apr 17 ONT + PacBio / Common analyses 3: Genome assembly
27 Apr 22 Common analyses 4: Protein structure prediction
28 Apr 24 Common analyses 5: Transcriptomics
30 Apr 29 Common analyses 6: Metagenomics
31 May 1 Project presentations

ECoS Teaching Statement

In an examination setting, unless the instructor gives explicit prior instructions to the contrary, violations of academic integrity shall consist of any attempt to receive assistance from written or printed aids, from any person or papers or electronic devices, or of any attempt to give assistance, whether the student doing so has completed his or her own work or not. Other violations include, but are not limited to, any attempt to gain an unfair advantage in regard to an examination, such as tampering with a graded exam or claiming another's work to be one's own. Other assessments (including ANGEL-administered quizzes and assessments as well as homework assignments) are expected to represent your own independent work unless specifically stated otherwise. Failure to comply will lead to sanctions against the student in accordance with the Policy on Academic Integrity in the Eberly College of Science. The Eberly College of Science Code of Mutual Respect and Cooperation embodies the values that we hope our faculty, staff, and students possess and will endorse to make The Eberly College of Science a place where every individual feels respected and valued, as well as challenged and rewarded. The Eberly College of Science is committed to the academic success of students enrolled in the College's courses and undergraduate programs. When in need of help, students can utilize various College and University wide resources for learning assistance. Penn State welcomes students with disabilities into the University's educational programs. If you have a disability-related need for reasonable academic adjustments in this course, contact the Office for Disability Services (ODS) at 814-863-1807 (V/TTY). For further information regarding ODS, please visit the Office for Disability Services Web site. In order to receive consideration for course accommodations, you must contact ODS and provide documentation (see the documentation guidelines). If the documentation supports the need for academic adjustments, ODS will provide a letter identifying appropriate academic adjustments. Please share this letter and discuss the adjustments with your instructor as early in the course as possible. You must contact ODS and request academic adjustment letters at the beginning of each semester.

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