BOL: Related items

assemblytics: delta file to analyze alignments of an assembly to another assembly or a reference genome

Jit — Thu, 14 Jun 2018 07:31:00 -0500

Download and install MUMmer Align your assembly to a reference genome using nucmer (from MUMmer package) $ nucmer -maxmatch -l 100 -c 500 REFERENCE.fa ASSEMBLY.fa -prefix OUT Consult the MUMmer manual if you encounter problems Optional: Gzip the delta file to speed up upload (usually 2-4X faster) $ gzip OUT.delta Then use the OUT.delta.gz file for upload. Upload the .delta or delta.gz file (view example) to Assemblytics Important: Use only contigs rather than scaffolds from the assembly. This will prevent false positives when the number of Ns in the scaffolded sequence does not match perfectly to the distance in the reference. The unique sequence length required represents an anchor for determining if a sequence is unique enough to safely call variants from, which is an alternative to the mapping quality filter for read alignment. http://assemblytics.com/

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

NanoPack: visualizing and processing long-read sequencing data

Jit — Fri, 10 Aug 2018 18:41:34 -0500

The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.

https://academic.oup.com/bioinformatics/article/34/15/2666/4934939

Address of the bookmark: https://github.com/wdecoster/nanoQC

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/

Circos visualization !

BioStar — Mon, 06 Mar 2023 06:01:32 -0600

Circos visualization

https://wlcb.oit.uci.edu/modules/index.html

Address of the bookmark: https://wlcb.oit.uci.edu/NG-Circos

A web-based tool for sequence alignment statistics and innovative visualization

BioStar — Thu, 04 Apr 2024 01:44:50 -0500

AlignStatPlot, a new R package and online tool that is well-documented and easy-to usefor MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analy-ses on sequencing data and generates new visualisation methods for MSA results. Whencompared to currently available tools, AlignStatPlot provides a robust ability to handle andvisualise diversity data, while the online version will save time and encourage researchersto focus on explaining their findings. It is a simple tool that can be used in conjunction withpopulation genetics software (PDF) AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data.

Address of the bookmark: https://bioinformatics.um6p.ma/AlignStatPlot/

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/

DeepHiC: A Generative Adversarial Network for Enhancing Hi-C Data Resolution

Rahul Nayak — Tue, 03 Mar 2020 01:12:47 -0600

DeepHiC is a GAN-based model for enhancing Hi-C data resolution. We developed this server for helping researchers to enhance their own low-resolution data by a few steps of clicks. Ab initio training could be performed according to our published code. We provided trained models for various depth of low-coverage sequencing Hi-C data. The depth of input data is estimated by its distribution comparing with those of the downsampled Hi-C data we used in training

Address of the bookmark: http://sysomics.com/deephic

d3Network:Tools for creating D3 JavaScript network, tree, dendrogram, and Sankey graphs from R.

Jit — Fri, 06 Apr 2018 12:10:45 -0500

Mike Bostock’s D3.js is great for creating interactive network graphs with JavaScript. The d3Network package makes it easy to create these network graphs from R. The main idea is that you should able to take an R data frame with information about the relationships between members of a network and create full network graphs with one command.

Address of the bookmark: http://christophergandrud.github.io/d3Network/

DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Jit — Sat, 25 Jan 2020 13:28:09 -0600

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

GraphPath: A graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network

LEGE — Wed, 27 Mar 2024 20:51:21 -0500

Achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. In this paper, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling.

Address of the bookmark: https://github.com/amazingma/GraphPath