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Fall 2025

Bioinformatics

A theoretical-practical course introducing students to the computational analysis of clinical genomic data. It covers bioinformatics tools, genetic databases, data formats and handling large datasets, variant analysis, monogenic and polygenic risk models (GWAS and PRS), and results visualization, integrating scripting in Python, R, and the command line. By the end, students will be able to build bioinformatics pipelines and interpret genetic data with applications in personalized medicine.

Fall 2025

Google Classroom

Prerequisites

Molecular biology (basic concepts of DNA, RNA, and genes); basic statistics (p-values and hypothesis testing); basic computer skills (file organization and operating systems); prior programming experience in Python, R, or another language is required to keep up with the course.

Meeting Time
Mondays and Wednesdays, 4:00–6:00 PM
Location
Room IAT-06

Staff

Dr. Arturo López Pineda

Dr. Arturo López Pineda

Instructor

arturolp@enesmorelia.unam.mx

Syllabus

Download the full syllabus as a PDF.

Syllabus (PDF)

Topics

Fundamentals of Bioinformatics

  • History and applications
  • Fundamentals of Molecular Biology
  • Sequencing technologies such as NGS and Long Read Sequencing
  • Current challenges, bioethics, and privacy

Classical Bioinformatics

  • Sequence alignment
  • Programming Challenge 1: Running BLAST
  • Multiple alignment with Clustal Omega
  • Automating searches and alignments
  • Brief introduction to phylogenetics
  • Programming Challenge 2: Building a Phylogenetic Tree

Genomic Databases

  • Genomic databases such as NCBI, Ensembl, and UCSC Genome Browser
  • Variant databases such as dbSNP, dbVar, and GnomAD
  • Clinical databases such as ClinVar and ClinGen
  • Programming Challenge 3: Searching and Downloading Data

Fundamentals of Computational Genomics

  • Command line (Unix/Linux)
  • Text utilities (grep, cut, AWK)
  • Filtering and processing data with the CLI and scripting
  • Data formats such as FASTA and VCF
  • Programming Challenge 4: Working with AWK and bash

Population Analysis

  • Fundamentals of population genetics
  • PCA for population structure
  • Genomic data imputation
  • Genomic phasing
  • ADMIXTURE, STRUCTURE, NeuralAdmixture
  • Programming Challenge 5: Population Ancestry Analysis

Predictive Genomics

  • Monogenic risk models
  • Variant types (SNPs, indels, CNVs)
  • Genome-wide association studies (GWAS)
  • Polygenic Risk Score (PRS) models
  • Programming Challenge 6: GWAS and PRS

Grading

  • Biweekly Programming Challenges

    Hands-on programming exercises and real-data analysis aligned with the course topics. Each challenge is worth 12% of the final grade.

    80%

  • Final Theoretical-Practical Exam

    Written and practical evaluation at the end of the course, covering data interpretation, analysis, and key concepts in clinical bioinformatics.

    20%

Policies

  • Attendance carries no direct percentage weight in the final grade. However, a minimum of 80% attendance is required to be eligible for continuous assessment and the final ordinary exam.
  • All submitted work (assignments, presentations, and projects) must be original. Generative AI tools may be used only for research, brainstorming, or grammar review, but not to draft the final content. The instructor reserves the right to request an in-person oral defense of any submitted work; if the student cannot demonstrate mastery of the topic or authorship of the text during that defense, the activity will be voided (a grade of zero).

Readings

  • Mount, D. W. (2007). Bioinformatics: Sequence and Genome Analysis (2nd ed.). Cold Spring Harbor Laboratory Press.
  • Lesk, A. M. (2019). Introduction to Bioinformatics (5th ed.). Oxford University Press.
  • Xiong, J. (2020). Essential Bioinformatics (2nd ed.). Cambridge University Press.
  • Strachan, T., & Read, A. P. (2019). Human Molecular Genetics (5th ed.). Garland Science.
  • Feero, W. G., Guttmacher, A. E., & Collins, F. S. (Eds.). (2010). Genomic Medicine: A Primer. Cold Spring Harbor Laboratory Press.
  • Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016). Next-Generation Sequencing: Establishing Biomarkers for Clinical Diagnostics. CRC Press.
  • Coon, S. L., & Boyle, J. (2015). Learning Python for Bioinformatics. O'Reilly Media.
  • Homer, N., & Ericsen, A. (2014). Bioinformatics with R Cookbook. Packt Publishing.