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/

GMASS: a novel measure for genomeassembly structural similarity

Abhimanyu Singh — Sun, 14 Apr 2019 20:35:40 -0500

The GMASS score is a novel measure for representing structural similarity between two assemblies. It will contribute to the understanding of assembly output and developing de novo assemblers.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2710-z

Address of the bookmark: http://bioinfo.konkuk.ac.kr/GMASS/htdocs/syncircos.php

MEGAN6

Neel — Mon, 25 Jul 2016 05:45:22 -0500

Microbiome analysis using a single application

MEGAN6 is a comprehensive toolbox for interactively analyzing microbiome data. All the interactive tools you need in one application.

Taxonomic analysis using the NCBI taxonomy or a customized taxonomy such as SILVA
Functional analysis using InterPro2GO, SEED, eggNOG or KEGG
Bar charts, word clouds, Voronoi tree maps and many other charts
PCoA, clustering and networks
Supports metadata
MEGAN parses many different types of input

Why use MEGAN6?

The software is:

Easy to use. MEGAN6 is a single application and all features are available through menus, toolbars and graphics. No scripting skills required.
Powerful. MEGAN6 allows you to work with hundreds of samples containing hundreds of millions of sequencing reads. Blast-like analysis can be performed using DIAMOND.
Comprehensive. MEGAN6 offers a large range of analysis tools, and is under active development.

Address of the bookmark: https://ab.inf.uni-tuebingen.de/software/megan6

slacTree: SVG Large Annotated Circular Tree drawing

Jit — Mon, 03 Jul 2017 08:02:56 -0500

A simple, extensible, Perl script for producing figures of large phylogenetic trees.

While there are many other tree drawing programs, slacTree was originally written in 2009 to fill a need for producing publication quality figures of circular trees with more than 1000 taxa with custom annotations
Because it is a single Perl script with very few dependencies, it is easy to run, and easy to further customize
SVG is used because it is a scalable format allowing for very small representations of entire trees or highly magnified regions with unlimited resolution
Circular and radial trees are more compact than linear representations

Address of the bookmark: https://github.com/mccrowjp/slacTree

Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees

Rahul Nayak — Wed, 27 Nov 2019 05:32:33 -0600

The Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees (although clustering trees or any other tree-like data structure are also supported).

Other tools

https://github.com/shenwei356/taxonkit

ETE, version: 3.1.1
BioPython, version: 1.73
taxadb, version: 0.10.1
TaxonKit, version: 0.5.0

Address of the bookmark: https://pypi.org/project/ete3/3.1.1/

The Tree That Learned to Read Genomes

BioStar — Fri, 11 Sep 2026 00:51:02 -0500

Imagine trying to reconstruct a family tree when many family photographs are missing, some names are unclear, and different relatives remember the past differently. This is similar to the challenge scientists face when trying to understand how organisms are related. Their family records are genomes, and the family tree is a phylogenetic tree. With modern sequencing technologies, scientists now have access to thousands of genomes, but comparing them can be difficult, especially when genomes are incomplete or poorly annotated.

A recent study by Webster and Chapman introduces Buscogeny (https://github.com/Jwebster89/Buscogeny), an open-source bioinformatics pipeline designed to make this process easier. The tool uses BUSCO (Benchmarking Universal Single-Copy Orthologs), which identifies conserved genes that are expected to occur as single copies in particular groups of organisms. These genes act like evolutionary landmarks, allowing researchers to compare genomes and investigate their relationships.

Buscogeny brings several steps of phylogenomic analysis into one workflow. It identifies BUSCO genes, extracts and aligns their sequences, removes unreliable regions, combines information from multiple genes, and uses the resulting data to construct a phylogenetic tree. It also provides quality-control information, helping researchers identify genomes that may be too incomplete for reliable analysis.

The authors demonstrate the usefulness of Buscogeny with bacterial and fungal genomes, including Alternaria. This is particularly valuable because fungal genomes can vary greatly in quality and annotation. Ultimately, Buscogeny helps transform massive amounts of genomic data into an understandable evolutionary story. Each DNA sequence becomes a clue, each conserved gene becomes a landmark, and together these clues reveal a picture of how life is connected.

More at https://link.springer.com/article/10.1007/s10123-025-00752-6?

Free Genomics data !

BioStar — Fri, 07 Feb 2020 14:08:31 -0600

The specimens were collected by the Oxford Wytham Woods and Edinburgh Lohse lab teams. DNA extraction and sequencing was carried out by the Sanger Institute Scientific Operations teams. Assemblies were carried out by the Tree of Life team (Shane McCarthy) and colleagues in Pacific Biosciences (Jonas Korlach).

https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/

Address of the bookmark: https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/

Kaiju

Jit — Mon, 27 Jun 2016 11:23:04 -0500

Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.

By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database nr used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.

Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.

Kaiju is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Address of the bookmark: http://kaiju.binf.ku.dk/

Rules for Pango Lineage !

Abhi — Tue, 14 Dec 2021 04:40:26 -0600

All the rules to classify a Lineage !

https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

Address of the bookmark: https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

PureCN: copy number calling and SNV classification using targeted short read sequencing

Jit — Thu, 09 Aug 2018 04:09:37 -0500

This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.

Author: Markus Riester [aut, cre], Angad P. Singh [aut]

Maintainer: Markus Riester

Citation (from within R, enter citation("PureCN")):

Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D, Morrissey M (2016). “PureCN: Copy number calling and SNV classification using targeted short read sequencing.” Source Code for Biology and Medicine, 11, 13. doi: 10.1186/s13029-016-0060-z.

Address of the bookmark: http://bioconductor.org/packages/release/bioc/html/PureCN.html