A collection of similar but different presentations I've made aimed at introducing bioinformatics to bench biologists.

Dataset for the practice in the data preprocessing and unsupervised learning in the introduction to bioinformatics course

Modules showing how the NCBI database classifies and organizes information on DNA sequences, evolutionary relationships, and scientific publications. And a module working to identify a nucleotide sequence from an insect endosymbiont by using BLAST

“Bioinformatics: Introduction and Methods,” a Bilingual Massive Open Online Course (MOOC) as a New Example for Global Bioinformatics Education

This is an introduction to bioinformatics using hemoglobin as an example. The worksheets introduce students to resources to explore the DNA, RNA and polypeptide linear structure with a brief introduction to the quaternary structure of hemoglobin.

Subtitle

"Synthetic protein dataset with sequences, physical properties, and functional classification for machine learning tasks."

Description

Introduction

This synthetic dataset was created to explore and develop machine learning models in bioinformatics. It contains 20,000 synthetic proteins, each with an amino acid sequence, calculated physicochemical properties, and a functional classification.

Columns Included

• ID_Protein: Unique identifier for each protein.
• Sequence: String of amino acids.
• Molecular_Weight: Molecular weight calculated from the sequence.
• Isoelectric_Point: Estimated isoelectric point based on the sequence composition.
• Hydrophobicity: Average hydrophobicity calculated from the sequence.
• Total_Charge: Sum of the charges of the amino acids in the sequence.
• Polar_Proportion: Percentage of polar amino acids in the sequence.
• Nonpolar_Proportion: Percentage of nonpolar amino acids in the sequence.
• Sequence_Length: Total number of amino acids in the sequence.
• Class: The functional class of the protein, one of five categories: Enzyme, Transport, Structural, Receptor, Other.

Inspiration and Sources

While this is a simulated dataset, it was inspired by patterns observed in real protein datasets, such as: - UniProt: A comprehensive database of protein sequences and annotations. - Kyte-Doolittle Scale: Calculations of hydrophobicity. - Biopython: A tool for analyzing biological sequences.

Proposed Uses

This dataset is ideal for: - Training classification models for proteins. - Exploratory analysis of physicochemical properties of proteins. - Building machine learning pipelines in bioinformatics.

How This Dataset Was Created

1. Sequence Generation: Amino acid chains were randomly generated with lengths between 50 and 300 residues.
2. Property Calculation: Physicochemical properties were calculated using the Biopython library.
3. Class Assignment: Classes were randomly assigned for classification purposes.

Limitations

• The sequences and properties do not represent real proteins but follow patterns observed in natural proteins.
• The functional classes are simulated and do not correspond to actual biological characteristics.

Data Split

The dataset is divided into two subsets: - Training: 16,000 samples (proteinas_train.csv). - Testing: 4,000 samples (proteinas_test.csv).

Acknowledgment

This dataset was inspired by real bioinformatics challenges and designed to help researchers and developers explore machine learning applications in protein analysis.

The table provides a short description of the major components of the model employed by each course, highlighting any differences between the two (deviations are indicated by an asterisk (*)).

File used for the Introduction to bioinformatics (IBT) Linux practical session course.

Presentation on teaching introductory bioinformatics with Jupyter notebook-based active learning at the 2019 Great Lakes Bioinformatics Conference

all vcf files that I was able to provide in BLG348 Intro to Bioinformatics course term project. Mutect variantCaller didn't work properly so I didn't add them. NotFıltered vcf's indicates previos version of vcf's that contains different filters (not only PASS ones) You can also check my profile to see the plots that I used for my project report & presentation.

Familiarity with genome-scale data and the bioinformatic skills to analyze it have become essential for understanding and advancing modern biology and human health, yet many undergraduate biology majors are never exposed to hands-on bioinformatics. This paper presents a module that introduces students to applied bioinformatic analysis within the context of a research-based microbiology lab course. One of the most commonly used genomic analyses in biology is resequencing: determining the sequence of DNA bases in a derived strain of some organism, and comparing it to the known ancestral genome of that organism to better understand the phenotypic differences between them. Many existing CUREs — Course Based Undergraduate Research Experiences — evolve or select new strains of bacteria and compare them phenotypically to ancestral strains. This paper covers standardized strategies and procedures, accessible to undergraduates, for preparing and analyzing microbial whole-genome resequencing data to examine the genotypic differences between such strains. Wet-lab protocols and computational tutorials are provided, along with additional guidelines for educators, providing instructors without a next-generation sequencing or bioinformatics background the necessary information to incorporate whole-genome sequencing and command-line analysis into their class. This module introduces novice students to running software at the command-line, giving them exposure and familiarity with the types of tools that make up the vast majority of open-source scientific software used in contemporary biology. Completion of the module improves student attitudes toward computing, which may make them more likely to pursue further bioinformatics study.

Introductory slides for the UCSC Genome Browser. Part of a set of materials available for training on the UCSC tools. Also available is a recording of the same material as a video. Exercises to practice additional skills can also be used for the training. The full training suite is available: http://openhelix.com/ucsc and there is an additional set of materials with more advanced topics: http://www.openhelix.com/ucscadv . BTW: there is a full script in the "notes" area of the slides, but that is not visible in the viewer.

🔭 Overview

  R2MED: First Reasoning-Driven Medical Retrieval Benchmark

R2MED is a high-quality, high-resolution synthetic information retrieval (IR) dataset designed for medical scenarios. It contains 876 queries with three retrieval tasks, five medical scenarios, and twelve body systems.

Dataset

Q

D

Avg. Pos Q-Len D-Len

Biology 103 57359 3.6 115.2 83.6

Bioinformatics77 47473 2.9 273.8 150.5

Medical Sciences 88 34810 2.8 107.1 122.7

MedXpertQA-Exam 97… See the full description on the dataset page: https://huggingface.co/datasets/R2MED/Bioinformatics.

This Critical Guide in the Introduction to Bioinformatics series provides a brief outline of the Protein Data Bank – the PDB – the world’s primary repository of biological macromolecular structures.

Data and conda software environment file for the chapter 'Introduction to R and the Tidyverse' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).

This dataset is about book subjects. It has 1 row and is filtered where the books is Statistical methods in bioinformatics : an introduction. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This module is a computer-based introduction to bioinformatics resources. This easy-to-adopt module weaves together several important bioinformatic tools so students can grasp how each is used in answering research questions. Published in CBE-LSE

Vampirium setup testing

This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on Github:

Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments

Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline.

Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology.

Slides and raw_reads will be released in a later version.

Slides.zip contains all the slides used in the workshop.

Raw_reads.zip contains the raw reads from the bulk RNAseq experiment (10.1016/j.celrep.2014.10.054) used in this course.

This record contains the data files used in exercises in the NBIS course "Introduction to Data Management Practices".

In order to introduce students to the concept of molecular diversity, we developed a short, engaging online lesson using basic bioinformatics techniques. Students were introduced to basic bioinformatics while learning about local on-campus species diversity by 1) identifying species based on a given sequence (performing Basic Local Alignment Search Tool [BLAST] analysis) and 2) researching and documenting the natural history of each species identified in a concise write-up. To assess the student’s perception of this lesson, we surveyed students using a Likert scale and asking them to elaborate in written reflection on this activity. When combined, student responses indicated that 94% of students agreed this lesson helped them understand DNA barcoding and how it is used to identify species. The majority of students, 89.5%, reported they enjoyed the lesson and mainly provided positive feedback, including “It really opened my eyes to different species on campus by looking at DNA sequences”, “I loved searching information and discovering all this new information from a DNA sequence”, and finally, “the database was fun to navigate and identifying species felt like a cool puzzle.” Our results indicate this lesson both engaged and informed students on the use of DNA barcoding as a tool to identify local species biodiversity.

Primary Image: DNA Barcoded Specimens. Crane fly, dragonfly, ant, and spider identified using DNA barcoding.