BOL: Related items

Genome U-Plot: a whole genome visualization

Rahul Nayak — Fri, 13 Jul 2018 19:50:41 -0500

Genome U-Plot for producing clear and intuitive graphs that allows researchers to generate novel insights and hypotheses by visualizing SVs such as deletions, amplifications, and chromoanagenesis events. The main features of the Genome U-Plot are its layered layout, its high spatial resolution and its improved aesthetic qualities.

https://github.com/gaitat/GenomeUPlot

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

MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization

Jit — Fri, 24 Jan 2020 04:09:15 -0600

MitoZ is a Python3-based toolkit which aims to automatically filter pair-end raw data (fastq files), assemble genome, search for mitogenome sequences from the genome assembly result, annotate mitogenome (genbank file as result), and mitogenome visualization. MitoZ is available from https://github.com/linzhi2013/MitoZ.

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

Tools for Geospatial data analysis !

BioStar — Wed, 22 Mar 2023 02:10:28 -0500

Geospatial data is becoming increasingly important in many fields, including urban planning, environmental science, public health, and more. These tools can help you work with data from a variety of sources, including satellite imagery, GPS data, and other forms of spatial data. They can help you visualize data, perform complex analysis, and even create maps and other visualizations.

The list includes some of the most popular and widely used geospatial tools available in Python. These tools can help you work with data from a variety of sources and in a variety of formats. Some of the tools are focused on visualization, such as Cartopy, Folium, and Contextily, which allow you to create interactive maps and other visualizations. Other tools are more focused on data manipulation and analysis, such as Fiona, GeoPandas, and Rasterio, which allow you to manipulate and analyze spatial data in a variety of ways.

The list also includes some tools for working with specific types of geospatial data. For example, the H3 library is designed specifically for working with hexagonal grids, while PySAL is focused on spatial econometrics and spatial analysis. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.

In summary, this list is an excellent resource for anyone working with geospatial data in Python. It contains a wide range of tools for working with different types of data, and can help you visualize data, perform complex analysis, and create maps and other visualizations. If you're looking to enhance your skills in geospatial analysis, this list is definitely worth checking out.

These tools are:

ArcGIS - https://lnkd.in/dgC6sKJH
Cartopy - https://lnkd.in/dc8ijXRg
Contextily - https://lnkd.in/dTdQsmKX
Descartes - https://lnkd.in/dCJykxwW
Fiona - https://lnkd.in/d8sJ3Q5a
Folium - https://lnkd.in/dfSsE-MB
GDAL - https://lnkd.in/dYBJBaAY
Geohash - https://lnkd.in/d_NxJ4_M
GeoJSON - https://lnkd.in/daGs2WYq
GeoPandas - https://lnkd.in/dBTFKKV3
Geopy - https://lnkd.in/dfAzR8Xa
Gevent - http://www.gevent.org
H3 - https://h3geo.org/docs/
OSMnx - https://lnkd.in/dm3pHgUS
PyQGIS - https://lnkd.in/dShWyWVr
PySAL - https://pysal.org
Pydeck - https://lnkd.in/dGBFu-iw
Pyproj - https://lnkd.in/dNG9fdkm
RTree - https://lnkd.in/dURMiYpU
Rasterio - https://lnkd.in/dEMC6ve6
Scikit-mobility - https://lnkd.in/dpHhaX2J
Shapely - https://lnkd.in/d568datK

These tools offer a wide range of capabilities for working with geospatial data, from visualizing and manipulating data to performing complex analysis and modeling. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.

GenoFig: a user-friendly application for the visualization and comparison of genomic regions

BioStar — Mon, 05 Aug 2024 23:06:58 -0500

Tool for graphical vizualisation of annotated genetic regions, and homologous regions comparison. It is an independent recoding of Easyfig 2 initially developped by at the S. Beatson Lab [https://mjsull.github.io/Easyfig/]

Download the GenoFig source code using the 'Download' button on top of this page. Cloning is currently not available for people not member of the INRAE French Institution. After decompression, open a terminal in the folder containing the decompressed files and run:

conda env create -f extras/requirements.yml
extras/SETUP.sh

Address of the bookmark: https://forgemia.inra.fr/public-pgba/genofig

GPOPSIM: a simulation tool for whole-genome genetic data

Jit — Wed, 17 Jan 2018 03:47:46 -0600

GPOPSIM is a simulation tool for pedigree, phenotypes, and genomic data, with a variety of population and genome structures and trait genetic architectures. It provides flexible parameter settings for a wide discipline of users, especially can simulate multiple genetically correlated traits with desired genetic parameters and underlying genetic architectures.

Address of the bookmark: https://github.com/SCAU-AnimalGenetics/GPOPSIM

BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data

Jit — Thu, 31 May 2018 09:35:23 -0500

BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes. The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC. https://github.com/lh3/bfc

Address of the bookmark: https://github.com/lh3/bfc

ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data

BioStar — Thu, 20 Dec 2018 12:03:32 -0600

The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps: prune, partition, rescue, optimize and build.

Address of the bookmark: https://github.com/tangerzhang/ALLHiC/wiki

Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies' sequencing data

Jit — Tue, 22 Jan 2019 06:26:55 -0600

Run a pipeline processing fast5s to a consensus in a single command.
Recommended fixed "standard" and "fast" pipelines.
Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.
Seemless distribution of tasks over local or distributed compute.
Highly configurable.
Open source (Mozilla Public License 2.0).

Documentation can be found at https://nanoporetech.github.io/katuali/.

Address of the bookmark: https://github.com/nanoporetech/katuali

ClinCNV: Detection of copy number changes in Germline/Trio/Somatic contexts in NGS data

Neel — Thu, 16 Jan 2020 23:16:02 -0600

ClinCNV detects CNVs in germline and somatic context in NGS data (targeted and whole-genome). We work in cohorts, so it makes sense to try ClinCNV if you have more than 10 samples (recommended amount - 40 since we estimate variances from the data). By "cohort" we mean samples sequenced with the same enrichment kit with approximately the same depth (ie 1x WGS and 30x WGS better be analysed in separate runs of ClinCNV). Of course it is better if your samples were sequenced within the same sequencing facility.

Address of the bookmark: https://github.com/imgag/ClinCNV

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/