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CompBio Skills Seminar UC Berkeley
เข้าร่วมเมื่อ 10 ก.ย. 2020
SciDataFlow: A Tool for Improving the Flow of Data through Science
Details: Wednesday, May 8, 12:00 - 1:00pm
Presenter: Vince Buffalo, Nielsen Lab, UC Berkeley
More at ccbskillssem.github.io/
Presenter: Vince Buffalo, Nielsen Lab, UC Berkeley
More at ccbskillssem.github.io/
มุมมอง: 112
วีดีโอ
Using SLiM for forward genetic simulation
มุมมอง 1763 หลายเดือนก่อน
Details: Thursday, April 25, 12:00 - 1:00pm Presenter: Kailey Ferger, Tsutsui Lab, UC Berkeley More at ccbskillssem.github.io/
Leveraging Object-Oriented Programming (OOP) in Python for Bioinformatics
มุมมอง 1044 หลายเดือนก่อน
Details: Wednesday, February 14, 12:00 - 1:00pm Presenter: Carmelle Catamura, Lareau Lab, UC Berkeley More at ccbskillssem.github.io/
Estimating and visualizing population genetic structure for landscape genomics
มุมมอง 1668 หลายเดือนก่อน
Details: Thursday, December 14, 12:00 - 1:00pm Presenter: George Zaragoza, Fitak Lab, University of Central Florida More at ccbskillssem.github.io/
An overview of skills and pipelines for A-Z reproducible manuscripts
มุมมอง 1449 หลายเดือนก่อน
Details: Wednesday, November 8, 12:00 - 1:00pm Presenter: Juan Manuel "Manny" Vazquez, Sudmant Lab, University of California, Berkeley More at ccbskillssem.github.io/
An introduction to snakemake: a tool for automating and streamlining your analyses
มุมมอง 1.1K10 หลายเดือนก่อน
Details: Thursday, October 12, 12:00 - 1:00pm Presenter: Stacy Li, Sudmant Lab, University of California, Berkeley More at ccbskillssem.github.io/
Building an imputation pipeline for low coverage ancient genomes
มุมมอง 316ปีที่แล้ว
Details: Thursday, April 13, 12:30 - 1:30pm Presenter: Kiran Kumar, Zoellner Lab, University of Michigan More at ccbskillssem.github.io/
Unix tools, scripting, and parallel computing
มุมมอง 103ปีที่แล้ว
Details: Thursday, April 6, 12:30 - 1:30pm Presenter: Lenore Pipes, Nielsen Lab, UC Berkeley More at ccbskillssem.github.io/
Leveraging R's ggplot for biological data analysis and visualization
มุมมอง 191ปีที่แล้ว
Details: Thursday, February 2, 12:30 - 1:30pm Presenter: Sarah Johnson, Moorjani Lab, UC Berkeley More at ccbskillssem.github.io/
Introduction to modelling single-cell dynamics
มุมมอง 500ปีที่แล้ว
Details: Thursday, Novermber 10, 12:30 - 1:30pm Presenter: Michael Vineyard, Massachusetts General Hospital/Broad Institute More at ccbskillssem.github.io/
Introduction to LaTeX using Overleaf
มุมมอง 231ปีที่แล้ว
Topic: Introduction to LaTeX using Overleaf Details: Thursday, October 13, 12:30 - 1:30pm Presenter: Elise Kerdoncuff, UC Berkeley More info: ccbskilssem.github.io
UC Berkley CCB Skills Seminar 05/11/22: "Introduction to scikit-image for 3D image analysis"
มุมมอง 2552 ปีที่แล้ว
UC Berkeley Center for Computational Biology Skills Seminar from May 11, 2022. Alexandre de Siqueira (BIDS Postdoctoral Fellow, UC Berkeley) gives a tutorial on the python-based scikit-image software package, using a microscopy dataset of cells as an example.
Using Github Actions for collaborative development projects
มุมมอง 992 ปีที่แล้ว
Topic: Using Github Actions for collaborative development projects Details: Wednesday, April 13, 12 - 1pm Presenter: Valeh Valiollah Pour Amiri, Research Engineer, UC Berkeley Materials: see ccbskillssem.github.io/ Apologies for poor screen capture quality.
Introduction to computational population genetics
มุมมอง 2912 ปีที่แล้ว
Details: Wednesday, March 9, 12 - 1pm Presenter: Yun Deng, CCB, UC Berkeley Materials at: ccbskillssem.github.io/
CCB Skills Seminar 2/9/22: "Intro to R's Tidyverse for Data Science"
มุมมอง 1932 ปีที่แล้ว
UC Berkeley Center for Computational Biology (CCB) Skills Seminar from 2/9/22. CCB's Monica Arnielle provides a tutorial on getting started with R's tidyverse and how to do basic data science with these modules. To access the resources for this talk, view past seminars, or see the upcoming schedule, please check out our CCB Skills Seminar website: ccbskillssem.github.io/
Introduction to Pyro, a probabilistic programming language in Python
มุมมอง 2.1K2 ปีที่แล้ว
Introduction to Pyro, a probabilistic programming language in Python
Single-cell and spatial transcriptomics data analysis with Seurat in R
มุมมอง 6K2 ปีที่แล้ว
Single-cell and spatial transcriptomics data analysis with Seurat in R
Making and hosting your personal website using Jekyll and Github pages
มุมมอง 8K2 ปีที่แล้ว
Making and hosting your personal website using Jekyll and Github pages
Fast and efficient preprocessing of single-cell RNA-seq with kallisto | bustools
มุมมอง 1.6K2 ปีที่แล้ว
Fast and efficient preprocessing of single-cell RNA-seq with kallisto | bustools
Single-cell data analysis with Scanpy and scvi-tools
มุมมอง 12K3 ปีที่แล้ว
Single-cell data analysis with Scanpy and scvi-tools
Building your first Variational Autoencoder with PyTorch
มุมมอง 12K3 ปีที่แล้ว
Building your first Variational Autoencoder with PyTorch
UC Berkeley CCB Skills Seminar - "Deep learning for protein structure prediction with SidechainNet"
มุมมอง 4463 ปีที่แล้ว
UC Berkeley CCB Skills Seminar - "Deep learning for protein structure prediction with SidechainNet"
UC Berkeley CCB Skills Seminar: High Performance Computing with AWS
มุมมอง 1763 ปีที่แล้ว
UC Berkeley CCB Skills Seminar: High Performance Computing with AWS
Building reproducible software packages with Python, pip, and conda
มุมมอง 5093 ปีที่แล้ว
Building reproducible software packages with Python, pip, and conda
SLiM: Selection on Linked Mutations (in case you didn’t get a chance to look it up).
Thanks, this was helpful.
Hi! Can you detail on why snakemake knows that tasks should be run in a sequential manner? Particularly for variant calling this is very important. Best Regards!
Very Impressive.
thanks. a very helpful video
Where is inference code man
Wow this brings back so many memories! 😊😊Thanks for putting this presentation together 🙏🙏 I don't often see LaTeX talks on TH-cam. I wrote most of my homework and papers in LaTeX in college because I loved how neat and professional the output was 😊😊
drive.google.com/drive/folders/1EowAfwlIy2qwHHoJ-evAPmydrAiwNW9f Here is the MNIST dataset processed folder incase
This one is missing the training data now.
Go tigers Graham
Thanks for this guide! Like another commenter asked, how do you get the google verification HTML code? I'm struggling with that part!
Really nice overview! Would the colab notebook be available?
Wonderful work...Thank you so much, guys ❣
Do you have link, for the Jupyter Notebook?
colab.research.google.com/drive/18ltGv1hcDOAXz29hANLUizbuV438qDFs#scrollTo=Mkr46ZbTQ7E5
If you get an error like "no permissions" and you're on Linux, you probably have to use "sudo" before the command, then you have admin-rights.
Is the code available anywhere?
also thank you - this is an absolute goldmine!
the code could be shared please?
Hey, how did you get that google verification .html in 44:48? Thanks!
awesome talk! thanks for sharing
Good stuff!
update: there is a version for Linux Ubuntu!
Is there a plan to make a demo to go through the whole scvi-tools toolkit? It has become so massive that it becomes quite daunting to use it as a methods developer.
Excellent! Well done.
The downloaded MNIST folder only have a folder named "raw" inside. Would you please help? Are you explaining this in the later part of the video?
No need to look into the actual contents of the MNIST folder. The torchvision.datasets.MNIST function automatically searches for a folder called "MNIST" in the root directory specified and will go into the "raw" folder to extract the data. So if your Jupyter Notebook is on your desktop and you put the "MNIST" folder in a "Data" folder, this will work: train_dataset = torchvision.datasets.MNIST(root="Data", train=True, download=False)
great, tnx for tutorial!
Coming from Python, this presentation was a striking primer on why I keep an eye on Julia! At 31:10, the lack of a multiplication sign between the number and units (e.g. 2.5μm) caught my eye. Looking through the docs, I see this is possible since Julia has implicit multiplication (docs.julialang.org/en/v1/manual/integers-and-floating-point-numbers/): > To make common numeric formulae and expressions clearer, Julia allows variables to be immediately preceded by a numeric literal, implying multiplication ... The precedence of numeric literal coefficients used for implicit multiplication is higher than other binary operators such as multiplication (*) I've used Pint for units in Python (pint.readthedocs.io/en/stable/), which was nice, but seems like the syntax in Julia is cleaner and speed faster.