BOL: Related items

ShadowCaster: a hybrid approach for the detection of horizontal gene transfer events in prokaryotes

Jit — Tue, 14 Jul 2020 06:42:10 -0500

ShadowCaster implements an evolutionary model to calculate Bayesian likelihoods for each ‘alien genes’ with an unusual sequence composition according to the host genome background to detect HGT events in prokaryotes.

https://www.mdpi.com/2073-4425/11/7/756/htm

https://shadowcaster.readthedocs.io/en/latest/

https://github.com/dani2s/ShadowCaster_testData

Address of the bookmark: https://github.com/dani2s/ShadowCaster

homologizer: Phylogenetic phasing of gene copies into polyploid subgenomes

Abhi — Sat, 03 Jun 2023 19:19:10 -0500

This tutorial describes the usage of homologizer to phase gene copies into polyploid subgenomes. The tutorial is an abbreviated version of a soon-to-be published paper in Methods in Molecular Biology. Please see that paper for many more details and practical considerations for running homologizer analyses. If you use homologizer, please cite the paper in which we first describe the method:

Freyman, W.A., Johnson, M.G., and C.J. Rothfels. 2022. Homologizer: phylogenetic phasing of gene copies into polyploid subgenomes. bioRxiv 2020.10.22.351486v4

homologizer is implemented in RevBayes. Please see http://revbayes.com to download and install RevBayes. For users without previous RevBayes experience, we recommend the tutorials at http://revbayes.com.

Address of the bookmark: https://github.com/wf8/homologizer

ClueGO

Jit — Thu, 02 Jun 2016 09:51:44 -0500

ClueGO is a Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network and it can be used in combination with GOlorize.

Address of the bookmark: http://www.ici.upmc.fr/cluego/

Enrichr: a comprehensive gene set enrichment analysis

Jit — Thu, 27 Apr 2017 05:42:09 -0500

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw377

Address of the bookmark: http://amp.pharm.mssm.edu/Enrichr/

ARC: pipeline which facilitates iterative, reference guided de novo assemblies

Jit — Thu, 26 Jul 2018 09:20:26 -0500

ARC is a pipeline which facilitates iterative, reference guided de novo assemblies with the intent of:

Reducing time in analysis and increasing accuracy of results by only considering those reads which should assemble together.
Reducing/removing reference bias as compared to mapping based approaches.

The software is designed to work in situations where a whole-genome assembly is not the objective, but rather when the researcher wishes to assemble discreet 'targets' contained within next-generation shotgun sequence data. ARC decomplexifies the traditionally difficult problem of assembly by breaking the reads into small, manageable subsets which can then be assembled quickly and efficiently in parallel. Applications include those in which the researcher wishes to de novo assemble specific content and a set of semi-similar reference targets is available to initialize the assembly process.

https://ibest.github.io/ARC/

Address of the bookmark: https://ibest.github.io/ARC/

Pimp your brain: Bioinformatics

Wed, 20 Aug 2014 22:09:21 -0500

Jan Lisec from the Max Planck Institute of Molecular Plant Physiology explains, in this "pimp your brain" episode, what bioinformatics is and why bioinformatics is so important and indispensable for biological research. In the video serial "Pimp your brain" scientists from the Max Planck Institute of Molecular Plant Physiology describe their research. More videos from the 'Pimp your brain' serial are available on www.youtube.com/playlist?list=PL-l9VItC9Gn2Ur2Xj6PTOAkjLUlVPbIOO More videos are available on www.mpimp-golm.mpg.de

Ten quick tips for deep learning in biology

Neel — Fri, 25 Mar 2022 18:35:12 -0500

By taking a comprehensive and careful approach to deep learning based on critical thinking about research questions, planning to maintain rigor, and discerning how work might have far-reaching consequences with ethical dimensions, the life science community can advance reproducible, interpretable, and high-quality science that is enriching and beneficial for both scientists and society.

Address of the bookmark: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009803

Free Books on Machine Learning and Artificial Intelligent !

BioStar — Thu, 16 Mar 2023 00:10:24 -0500

An Introduction to Statistical Learning
This book provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

https://hastie.su.domains/ISLR2/ISLRv2_website.pdf

Python Data Science Handbook
You’ll learn how to use the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. This resource is perfect for tackling day-to-day issues such as cleaning, manipulating, and transforming data — or building machine learning models.

https://jakevdp.github.io/PythonDataScienceHandbook/

Dive into Deep Learning
Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 400 universities from 60 countries

https://d2l.ai/

Approaching (Almost) Any Machine Learning Problem
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is for you if you are looking for guidance on approaching machine learning problems.

https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf

MOSS: A System for Detecting Software Similarity

Jit — Sat, 09 Dec 2017 08:59:07 -0600

Moss (for a Measure Of Software Similarity) is an automatic system for determining the similarity of programs. To date, the main application of Moss has been in detecting plagiarism in programming classes. Since its development in 1994, Moss has been very effective in this role. The algorithm behind moss is a significant improvement over other cheating detection algorithms (at least, over those known to us).

Moss can currently analyze code written in the following languages:

C, C++, Java, C#, Python, Visual Basic, Javascript, FORTRAN, ML, Haskell, Lisp, Scheme, Pascal, Modula2, Ada, Perl, TCL, Matlab, VHDL, Verilog, Spice, MIPS assembly, a8086 assembly, a8086 assembly, MIPS assembly, HCL2.

Address of the bookmark: https://theory.stanford.edu/~aiken/moss/

kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity

Rahul Nayak — Tue, 29 May 2018 08:37:53 -0500

The k-mer Weighted Inner Product.

This software implements a de novo, alignment free measure of sample genetic dissimilarity which operates upon raw sequencing reads. It is able to calculate the genetic dissimilarity between samples without any reference genome, and without assembling one.

De novo estimates of genetic relatedness from next-gen sequencing data https://kwip.readthedocs.org

Address of the bookmark: https://github.com/kdmurray91/kwip