bioinformatics   12396

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GRAKN.AI - The Knowledge Graph DB
Nice looking commercial semweb products with a focus on bioinformatics. A little too buzzwordy with AI and knowledge graph. They have built their own alternative to sparql and tout similar benefits of automated reasoning. They have open sourced the query engine and other lower level components. They have a nice IDE for interacting with their db and some great visualizations
meta: Tempted to tag with a commercial-semweb concept as they have their own alternative to semweb
database  bioinformatics  united-kingdom 
4 days ago by cldwalker
Introduction to single-cell RNA-seq technologies
Slides from Lior Pachter class on single cell sequencing. Notes on blog post are useful.
bioinformatics  single_cell 
6 days ago by cdeboever3
Learning from protein fitness landscapes: a review of mutability, epistasis, and evolution - ScienceDirect
Proteins carry out many diverse functions in nature and are increasingly used in non-native contexts, such as in medical or industrial applications. A wide array of synthetic biology techniques can be used both to study proteins in their native context and to identify new variants with useful properties for non-native functions. High-resolution protein fitness landscapes, generated via deep scanning mutagenesis, are an emerging technology that can be used to model evolution and identify useful variants. Interestingly, many differences exist between mutability quantified by evolutionary studies and deep scanning mutagenesis. Here, we review several contributing factors to this difference, highlighting epistasis, binding partners, and selection conditions as key contributors. Through this lens, we describe what can be learned, both about evolution and protein function more broadly, from fitness landscape studies.
cannot-access  dammit  fitness-landscapes  scanning-mutagenesis  bioinformatics  theoretical-biology  looking-to-see 
7 days ago by Vaguery
The Math That Tells Cells What They Are | Quanta Magazine
That prompted a group at Princeton University, led by the biophysicists Thomas Gregor and William Bialek, to suspect something else: that the cells could instead get all the information they needed to define the positions of pair-rule stripes from the expression levels of the gap genes alone, even though those are not periodic and therefore not an obvious source for such precise instructions.

And that’s just what they found.

Over the course of 12 years, they measured morphogen and gap-gene protein concentrations, cell by cell, from one embryo to the next, to determine how all four gap genes were most likely to be expressed at every position along the head-to-tail axis. From those probability distributions, they built a “dictionary,” or decoder — an explicit map that could spit out a probabilistic estimate of a cell’s position based on its gap-gene protein concentration levels.

Around five years ago, the researchers determined this mapping by assuming it worked like what’s known as an optimal Bayesian decoder (that is, the decoder used Bayes’ rule for inferring the likelihood of an event from prior conditional probabilities). The Bayesian framework allowed them to flip the “unknowns,” the conditions of probability: Their measurements of gap gene expression, given position, could be used to generate a “best guess” of position, given only gap gene expression.
bioinformatics  geolocation  bayesian 
10 days ago by euler
g++ compiler for STAR aligner v 2.7.0e - Google Groups |
Google Groups allows you to create and participate in online forums and email-based groups with a rich experience for community conversations.
build  fromsource  c++11  bioinformatics  star  aligner 
15 days ago by kme
Building and installing · pezmaster31/bamtools Wiki · GitHub
C++ API & command-line toolkit for working with BAM data - pezmaster31/bamtools
bioinformatics  genome  rnaseq 
24 days ago by tvalerius

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