BOL: Related items

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/

Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences

Neel — Thu, 14 Aug 2025 04:02:05 -0500

Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.

The performance of the Jaeger workflow can be significantly increased by utilizing GPUs. To enable GPU support, the CUDA Toolkit and cuDNN library must be accessible to conda.

# setup bioconda
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict

# create conda environment and install jaeger
mamba create -n jaeger -c nvidia -c conda-forge cuda-nvcc "python>=3.9,<3.12" pip jaeger-bio


# activate environment
conda activate jaeger

Address of the bookmark: https://github.com/MGXlab/Jaeger

Expression profiling of the wheat pathogen Zymoseptoria tritici

Rahul Agarwal — Tue, 27 May 2014 17:09:24 -0500

Address of the bookmark: http://gbe.oxfordjournals.org/content/early/2014/05/14/gbe.evu101.abstract

Ryan E. Mills Lab

Tue, 26 May 2015 09:29:24 -0500

Our research group is primarily focused on the analysis of whole genome sequence data to identify genetic variation (primarily structural variation) and examine their potential functional impact in disease phenotypes. We are particularly interested in analyzing complex regions of the genome that are not easily resolved through modern sequencing approaches and which may exhibit interesting mechanistic origins.

We are also interested in the large-scale integration of genomic, expression, methylation and proteomic data sets, as well as the application of whole genome sequence analysis in clinical diagnostics.

More at http://millslab.ccmb.med.umich.edu/index.html

RNAseq data analysis links !

Robert M Willioms — Mon, 27 Nov 2017 16:28:11 -0600

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.

A survey of best practices for RNA-seq data analysis

RNA-seq workflow: gene-level exploratory analysis and DE

RNAseq analysis notes from Tommy Tang

Analysis of RNA ‐ Seq Data

RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

EBI RNA-Seq exercise

An open RNA-Seq data analysis pipeline tutorial with an example

RNA-Seq Analysis Workflow

Transcript-level expression analysis of RNA-seq experiments

Structural variants PPT

Jit — Wed, 07 Sep 2016 03:16:09 -0500

1000 Genomes data tutorial at ASHG

Structural variants presentation by

Jan Korbel

European Molecular Biology Laboratory (EMBL) Heidelberg Genome Biology Research Unit

Reference:

https://www.genome.gov/pages/research/der/1000genomesprojecttutorials/structuralvariants-jankorbel.pdf

Shinyheatmap

Jit — Fri, 21 Oct 2016 05:12:11 -0500

Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap.

More at http://biorxiv.org/content/early/2016/09/21/076463

Address of the bookmark: http://shinyheatmap.com/

REVIGO: Reduced Visualize gene ontology

Jit — Tue, 31 Jul 2018 05:28:42 -0500

REViGO can take long lists of Gene Ontology terms and summarize them by removing redundant GO terms. The remaining terms can be visualized in semantic similarity-based scatterplots, interactive graphs, or tag clouds. More about REViGO... |

Please enter a list of Gene Ontology IDs below, each on its own line. The GO IDs may be followed by p-values or another quantity which describes the GO term in a way meaningful to you.

Address of the bookmark: http://revigo.irb.hr/

U-Plot: Genome U-Plot sample implementation

Rahul Nayak — Tue, 03 Mar 2020 01:39:12 -0600

The Genome U-Plot is a JavaScript tool to visualize Chromosomal abnormalities in the Human Genome using a U-shape layout.

Address of the bookmark: https://github.com/gaitat/GenomeUPlot

CGView.js is a Circular Genome Viewing tool

LEGE — Wed, 27 Mar 2024 11:16:24 -0500

CGView.js is a Circular Genome Viewing tool for visualizing and interacting with small genomes. This software is an adaptation of the Java program CGView.

CGView.js is the genome viewer of Proksee, an expert system for genome assembly, annotation and visualization.

Features

Circular and linear views of genomes
Capable of drawing genomes up to 10 Mbp with 1000's of features and 100's contigs
Smooth zooming down to the sequence level
Easily generate features and plots directly form the sequence (e.g. ORFs, GC-content and GC-Skew)
Save high resolution PNG maps up to 8000x8000px
Fully documented API for interacting with CGView.js maps

Address of the bookmark: https://js.cgview.ca/