BOL: Related items

BioKit: a set of tools dedicated to bioinformatics, data visualisation

Neel — Tue, 18 Jun 2024 02:04:39 -0500

BioKit is a set of tools dedicated to bioinformatics, data visualisation (biokit.viz), access to online biological data (e.g. UniProt, NCBI thanks to bioservices). It also contains more advanced tools related to data analysis (e.g., biokit.stats). Since R is quite common in bioinformatics, we also provide a convenient module to run R inside your Python scripts or shell (:mod:biokit.rtools module).

Address of the bookmark: https://biokit.readthedocs.io/en/latest/index.html

BakRep (Denglish blend of Bakterien & Repository) simplifies access to this data

Neel — Wed, 13 Aug 2025 02:31:28 -0500

2,438,386 bacterial genomes at your fingertips consistently processed & characterized, enriched with metadata, accessible via a flexible search engine.

BakRep (Denglish blend of Bakterien & Repository) simplifies access to this data. It integrates enriched genomic information with metadata accessible via a flexible search-engine.

Key features

Assembly statistics: ensure data quality with genome-based key metrics
Taxonomic classification: robust, purely genome-based classifications (GTDB)
MLST: subtyping for deeper insights into genetic variation
Annotation: comprehensive & taxonomy-independent (Bakta)
Metadata: full original submission records

Address of the bookmark: https://bakrep.computational.bio/

GA4GH Data Working Group

Jitendra Prajapati — Sun, 20 Mar 2016 23:13:07 -0500

GA4GH Data Working Group

Led by David Haussler (UCSC) and Richard Durbin (Sanger Institute), the Data Working Group (DWG) of the Global Alliance brings together the leading Genome Institutes and Centers with IT industry leaders to create global standards and tools for the secure, privacy respecting and interoperable sharing of Genomic data.

More at http://ga4gh.org/#/

Address of the bookmark: http://ga4gh.org/#/

Platypus: A Haplotype-Based Variant Caller For Next Generation Sequence Data

Shruti Paniwala — Thu, 25 Oct 2018 06:14:55 -0500

Platypus is a tool designed for efficient and accurate variant-detection in high-throughput sequencing data. By using local realignment of reads and local assembly it achieves both high sensitivity and high specificity. Platypus can detect SNPs, MNPs, short indels, replacements and (using the assembly option) deletions up to several kb. It has been extensively tested on whole-genome, exon-capture, and targeted capture data, it has been run on very large datasets as part of the Thousand Genomes and WGS500 projects, and is being used in clinical sequencing trials in the Mainstreaming Cancer Genetics programme.

Tutorial https://github.com/andyrimmer/Platypus/blob/master/misc/README.txt

Address of the bookmark: http://www.well.ox.ac.uk/platypus

LRSDAY: Long-read Sequencing Data Analysis for Yeasts

Poonam Mahapatra — Mon, 26 Aug 2019 18:07:33 -0500

Long-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads.

Address of the bookmark: https://github.com/yjx1217/LRSDAY

BioJupies: Automatically Generates RNA-seq Data Analysis Notebooks

Rahul Nayak — Sun, 20 Dec 2020 11:43:45 -0600

With BioJupies you can produce in seconds a customized, reusable, and interactive report from your own raw or processed RNA-seq data through a simple user interface

BioJupies now supports user accounts! Sign in from the top right corner of the page for access to unlimited private notebooks, RNA-seq datasets and alignment jobs.

Address of the bookmark: https://amp.pharm.mssm.edu/biojupies/

Fundamentals of Data Visualization by Claus O. Wilke

Abhi — Sat, 08 Jun 2024 16:07:19 -0500

The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.

The entire book is written in R Markdown, using RStudio as my text editor and the bookdown package to turn a collection of markdown documents into a coherent whole. The book’s source code is hosted on GitHub, at https://github.com/clauswilke/dataviz.

Address of the bookmark: https://clauswilke.com/dataviz/

Graph Genome Suite

Jit — Fri, 28 Oct 2016 07:59:54 -0500

Seven Bridges is the biomedical data analysis company accelerating breakthroughs in genomics research for cancer, drug development and precision medicine. We build self-improving systems to analyze millions of genomes, including the Graph Genome Suite — the most advanced population genomics tools in the world.

Address of the bookmark: https://www.sbgenomics.com/graph/

PhyloGrapher - Graph Visualization Tool

Jit — Wed, 07 Mar 2018 18:11:25 -0600

PhyloGrapher is a program designed to visualize and study evolutionary relationships within families of homologous genes or proteins (elements). PhyloGrapher is a drawing tool that generates custom graphs for a given set of elements. In general, it is possible to use PhyloGrapher to visualize any type of relations between elements.

https://www.youtube.com/watch?v=WgufqYMHCvM

Address of the bookmark: http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html

PPanGGOLiN: Depicting microbial species diversity via a Partitioned PanGenome Graph Of Linked Neighbors

LEGE — Thu, 01 Feb 2024 00:24:32 -0600

PPanGGOLiN (Gautreau et al. 2020) is a software suite used to create and manipulate prokaryotic pangenomes from a set of either genomic DNA sequences or provided genome annotations. It is designed to scale up to tens of thousands of genomes. It has the specificity to partition the pangenome using a statistical approach rather than using fixed thresholds which gives it the ability to work with low-quality data such as Metagenomic Assembled Genomes (MAGs) or Single-cell Amplified Genomes (SAGs) thus taking advantage of large scale environmental studies and letting users study the pangenome of uncultivable species.

A complete documentation is available here.

Address of the bookmark: https://github.com/labgem/PPanGGOLiN