01:15:24

Using the ISMARA and CREMA web interfaces

01:15:32

CREMA: Theory and overview of the results

01:31:55

ISMARA: Theory and overview of the results

44:23

Enzymes: Extracting Biological Insight Using UniProt

12:25

Detecting inbreeding depression in structured populations

09:02

GeneSelectR: how to identify relevant features in RNA sequencing datasets

35:44

Wilhelm Dirks - Cell lines in the 21st century symposium

29:17

Tadashi Kondo - Cell lines in the 21st century symposium

30:08

Niels C. Bols - Cell lines in the 21st century symposium

31:56

Jan van der Valk - Cell lines in the 21st century symposium

24:27

Florian M. Wurm - Cell lines in the 21st century symposium

32:31

Anita Bandrowski - Cell lines in the 21st century symposium

33:40

Amos Bairoch - Cell lines in the 21st century symposium

31:40

Andreas Kurtz - Cell lines in the 21st century symposium

36:17

Amanda Capes Davis - Cell lines in the 21st century symposium

09:30

CellCharter: flexible and scalable spatial cell niches

01:04:14

An Introduction to Nextflow and nf-core

12:35

Easy simulation of species trees and populations over time using ReMASTER

06:36

How to boost your research with SIB’s Semantic Web of data

14:44

prolfqua: A comprehensive R package for protein differential expression analysis

24:21

Reading tabulated data with pandas (1 of 9)

29:36

Data manipulation: selecting rows and columns (2 of 9)

44:46

Data Analysis and Representation in Python – DataFrame subsetting (3 of 9)

31:07

Data Analysis and Representation in Python – Operations on columns (4 of 9)

05:25

Data Analysis and Representation in Python – Deleting and adding rows, and writing to file (5 of 9)

19:05

Data Analysis and Representation in Python – Basic description, common summary statistics (6 of 9)

21:45

Data Analysis and Representation in Python – Basic representation: plotting one column (7 of 9)

21:41

Data Analysis and Representation in Python – Accounting for categories in the data (8 of 9)

10:14

Data Analysis and Representation in Python – scatter/line plots, exporting, and conclusion (9 of 9)

02:00:21

Using ASAP for Single-Cell Analysis

39:51

Optimizing Python Code for Better Performance – Timing objects in python (1 of 8)

25:23

Optimizing Python Code for Better Performance – Time profiling in python (2 of 8)

35:02

Optimizing Python Code for Better Performance – Measuring RAM usage in python (3 of 8)

24:48

Optimizing Python Code for Better Performance – Speeding up your python code with numpy (4 of 8)

09:18

Optimizing Python Code for Better Performance – Speeding up your python code with numba (5 of 8)

24:08

Optimizing Python Code for Better Performance – Speeding up your python code with cython (6 of 8)

32:55

Optimizing Python Code for Better Performance – Practical_ numpy, numba, and cython (7 of 8)

39:00

Optimizing Python Code for Better Performance–Multiprocessing and multithreading in python (8 of 8)

13:32

Read2Tree: Inferring phylogenetic trees from raw sequencing data

02:26:59

Introduction to Deep Learning

53:29

Deep Learning techniques in Life Sciences

40:58

Supervised and reinforcement learning to measure biodiversity and guide conservation action

43:39

Leveraging the deep learning revolution to study the diversity of the catalogued protein universe

42:12

Physics-guided deep learning for the prediction of protein-ligand interactions

23:27

SIB Bioinformatics Award ceremony 2023 - PhD Paper Award - Viktor Petukhov

20:16

SIB Bioinformatics Award ceremony 2023 - Early Career Award - Maria Brbic

22:30

SIB Bioinformatics Award ceremony 2023 - Innovative Resource Award - OpenGenomeBrowser

16:27

MAPACHE - A flexible pipeline to map ancient DNA

01:37:40

NGS - Genome Variant analysis – Sequencing and alignment (2 of 5)

01:05:15

NGS - Genome Variant analysis – Variant calling (3 of 5)

10:54

NGS - Genome Variant analysis – Variant annotation (5 of 5)

35:05

NGS - Genome Variant analysis – Introduction to variant analysis (1 of 5)

08:08

NGS - Genome Variant analysis – Filtering and evaluation (4 of 5)

01:06:42

Single cell transcriptomics - Differential gene expression and Enrichment analysis (8 of 10)

35:14

Single cell transcriptomics - Trajectory analysis (10 of 10)

46:02

Single cell transcriptomics - Cell type annotation (7 of 10)

22:28

Single cell transcriptomics - Advanced analyses (9 of 10)

01:00:11

Single cell transcriptomics - Dimensionality reduction (4 of 10)

16:53

Single cell transcriptomics - Clustering (6 of 10)

24:21

Single cell transcriptomics - Integration (5 of 10)