Tag 1 | Beckstein Lab

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Tag Archives: mdanalysis
Research Experience for Undergraduates (Summer 2017)

Research Experience for Undergraduates (Summer 2017)

The Beckstein Lab offers a fully funded ten-week research program in computational biophysics for a highly motivated undergraduate student. This is a NSF-sponsored Research Experience for Undergraduates (REU). Deadline for applications is May 5, 2017.

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Google Summer of Code 2017: MDAnalysis

Google Summer of Code 2017: MDAnalysis

MDAnalysis is looking for students to participate in Google Summer of Code (GSoC) 2017. Application window is March 20 – April 3, 2017.

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Talks from Scipy 2016

Talks from Scipy 2016

This year at SciPy 2016 in Austin, TX, David Dotson presented his work on datreant : Persistent, Pythonic Trees for Heterogeneous Data and Oliver Beckstein talked about MDAnalysis : A Python Package for the Rapid Analysis of Molecular Dynamics Simulations. Video recordings of both talks are available here.

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SciPy 2016

SciPy 2016

Members of the Becksteinlab will present talks at SciPy 2016. This year the biggest conference on Python in science takes place from July 11-17 in Austin, TX. We will talk about datreant and MDAnalysis, get inspired, meet old and new friends, and hand out MDAnalysis stickers.

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Robert Delgado

Robert Delgado

Robert Delgado is an undergraduate pursuing a Bachelor’s of Science in Cornell’s Applied and Engineering Physics Program who is spending the summer of 2016 in the Beckstein lab as a REU student.

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Research Experience for Undergraduates (Summer 2016)

Research Experience for Undergraduates (Summer 2016)

The Beckstein Lab offers a fully funded ten-week research program in computational biophysics for a highly motivated undergraduate student. This is a NSF-sponsored Research Experience for Undergraduates. Deadline for applications is May 30, 2016.

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Google Summer of Code 2016: MDAnalysis

Google Summer of Code 2016: MDAnalysis

MDAnalysis is looking for students to participate in Google Summer of Code (GSoC) 2016. Start with the blog post MDAnalysis: Google Summer of Code 2016 .

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PSA: A Method for Quantifying Macromolecular Pathways

PSA: A Method for Quantifying Macromolecular Pathways

Transition pathways in high dimensional spaces, such as the ones produced by advanced algorithms to sample large conformational changes in macromolecules, are difficult to analyze quantitatively. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA is implemented in the MDAnalysis library.

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CECAM Macromolecular simulation software workshop

CECAM Macromolecular simulation software workshop

The CECAM Macromolecular simulation software workshop will be held from Mon 12 Oct, 2015 to Sat 24 Oct 2015 at Forschungszentrum Jülich, Germany. David Dotson and Oliver Beckstein will participate and will teach how to use MDAnalysis productively.

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Research Experience for Undergraduates (Summer 2015)

Research Experience for Undergraduates (Summer 2015)

The Beckstein Lab offers a fully funded ten-week research program in computational biophysics for a highly motivated undergraduate student. This is a NSF-sponsored Research Experience for Undergraduates. Deadline for applications is May 31, 2015.

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Middleware and High Performance Analytics Libraries for Scalable Data Science

Middleware and High Performance Analytics Libraries for Scalable Data Science

The Lab is a partner on the large NSF-sponsored SPIDAL project (“Middleware and High Performance Analytics Libraries for Scalable Data Science”) that was announced as part of a larger program for Laying the groundwork for data-driven science. The lab joins with partners at Indiana University, Emory University, Rutgers University, University of Kansas, University of Utah and Virginia Tech.

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Tutorial: MDAnalysis

Tutorial: MDAnalysis

A short introductory tutorial on MDAnalysis

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MDAnalysis

MDAnalysis

MDAnalysis is an open source, versatile, object-oriented Python library for analyzing molecular dynamics trajectories. It makes it easy to access trajectory data from Python code by interfacing trajectory readers (and writers) with NumPy arrays and to select atoms via a expressive selection syntax. The CHARMM/NAMD, Amber, Gromacs trajectory formats are all supported as well as PDB, GRO, CRD, PQR, and a range of others.

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