BOL: Related items

PheWAS: R package is designed to provide an accessible interface to the phenome wide association study

BioStar — Thu, 30 Jul 2020 22:06:37 -0500

The PheWAS R package is designed to provide an accessible interface to the phenome wide association study. For a description of the methods available and some simple examples, please see the package vignette or the R documentation. For installation help, see below. ##Installing the PheWAS Package The PheWAS package can be installed using the devtools package. The following code when executed in R will get you started:

install.packages("devtools")
#It may be necessary to install required as not all package dependencies are installed by devtools:
install.packages(c("dplyr","tidyr","ggplot2","MASS","meta","ggrepel","DT"))
devtools::install_github("PheWAS/PheWAS")
library(PheWAS)

Address of the bookmark: https://github.com/PheWAS/PheWAS

SEX-DETector: A Probabilistic Approach to Study Sex Chromosomes in Non-Model Organisms

Surabhi Chaudhary — Wed, 30 May 2018 15:57:31 -0500

SEX-DETector is a probabilistic method that relies on RNAseq data from a cross (parents and progeny of each sex) to infer autosomal and sex-linked genes (genes located on the non recombining part of sex chromosomes).

How does SEX-DETector work?

SEX-DETector does not require prior sequencing of a reference genome: the same sequencing data can be used for the assembly and for the mapping of the reads. A full documentation on the pipeline can be found here.

we recommend Trinity for the assembly.
Trinity components should be merged with cap3. Our code to perform the merging is available here.
We recommend BWA for mapping of the reads.
When the mapping has been perfomed, the individuals need to be genotyped; SEX-DETector takes files produced by Reads2snp (which is available for download on the PopPhyl website) as input.

Address of the bookmark: http://lbbe.univ-lyon1.fr/-SEX-DETector-.html?lang=eg

Genome Origami

Jitendra Narayan — Fri, 12 Dec 2014 22:48:17 -0600

There are several interesting factoid about our genomes, one of them is their folding. If we stretched out the DNA in a single cell, which is only a few millionths of an inch wide, it would span more than six feet. In other word, the size of six feet DNA fold themself to fit in a few millionths of an inch wide space. These DNA folding is a dynamic process that changes over time (!!). Researchers around the world have been trying to understand how DNA folds itself up so efficiently, and a recent post on the NIH Director’s Blog highlights new research illustrating how the human genome folds inside the cell’s nucleus, as well as how DNA folding affects gene regulation. The research team created this delightful video that demonstrates the principles involved using origami art.

http://bioinformaticsonline.com/videolist/watch/19555/a-3d-map-of-the-human-genome

Researchers have been working to determine how cells regulate gene expression for nearly as long as we’ve known about DNA. How, for example, do nerve cells know to turn off only nerve cell genes and turn off bone cell genes? DNA folding loops are part of the answer. This research team, which published their findings in a paper in Cell http://www.cell.com/cell/abstract/S0092-8674%2814%2901497-4 , found that the number of loops is much lower than expected. There are only 10,000 loops instead of the predicted millions, and they form on/off switches in DNA.

More at http://www.eurekalert.org/pub_releases/2014-12/ru-3mr121114.php

Reference http://www.cell.com/cell/abstract/S0092-8674%2814%2901497-4

Human Complete Genome

Shruti Paniwala — Wed, 06 Jul 2022 06:42:55 -0500

Telomere-to-telomere consortium

We have sequenced the CHM13hTERT human cell line with a number of technologies. Human genomic DNA was extracted from the cultured cell line. As the DNA is native, modified bases will be preserved. The data includes 30x PacBio HiFi, 120x coverage of Oxford Nanopore, 70x PacBio CLR, 50x 10X Genomics, as well as BioNano DLS and Arima Genomics HiC. Most raw data is available from this site, with the exception of the PacBio data which was generated by the University of Washington/PacBio and is available from NCBI SRA.

A UCSC browser is available for v2.0 (as well as legacy v1.0 and v1.1 versions). An interactive dotplot visualization of all genomic repeats is also available from resgen.io. Known issues identified in the assembly are tracked at CHM13 issues.

MORE at https://github.com/marbl/CHM13

Address of the bookmark: https://www.science.org/doi/10.1126/science.abj6987

TelomereHunter

Jit — Fri, 02 Feb 2018 04:23:59 -0600

TelomereHunter is a tool for estimating telomere content from human whole-genome sequencing data. It is designed to take BAM files from a tumor and a matching control sample as input. However, it is also possible to run TelomereHunter with one input file. TelomereHunter extracts and sorts telomeric reads from the input sample(s). For the estimation of telomere content, GC biases are taken into account. Finally, the results of TelomereHunter are visualized in several diagrams.

TelomereHunter is available for download at the following address: https://pypi.python.org/pypi/telomerehunter/

Address of the bookmark: http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html

SeqMule: Automated human exome/genome variants detection

BioStar — Tue, 18 Feb 2020 03:22:54 -0600

SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file.

Address of the bookmark: https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/

IPython: Interactive notebooks

Rahul Nayak — Fri, 07 Nov 2014 12:07:02 -0600

The IPython Notebook is a web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document.

These notebooks are normal files that can be shared with colleagues, converted to other formats such as HTML or PDF, etc. You can share any publicly available notebook by using the IPython Notebook Viewer service which will render it as a static web page. This makes it easy to give your colleagues a document they can read immediately without having to install anything.

To learn more about using the IPython Notebook, you can visit our example collection, and you can read the documentation for all the details on how to use and configure the system. The Notebook Gallery showcases many interesting notebooks covering a variety of topics, from basic programming to advanced scientific computing.

More http://www.nature.com/news/interactive-notebooks-sharing-the-code-1.16261

http://ipython.org/ipython-doc/1/interactive/notebook.html

Reference http://ipython.org/notebook.html

iTOL: Interactive Tree Of Life

Rahul Nayak — Tue, 18 Jul 2017 05:36:51 -0500

Interactive Tree Of Life is an online tool for the display, annotation and management of phylogenetic trees.

Explore your trees directly in the browser, and annotate them with various types of data.

iTOL can easily visualize trees with 50'000 or more leaves. With advanced search capabilities and display of unrooted, circular and regular cladograms or phylograms, exploring and navigating trees of any size is simple.

https://itol.embl.de/

Address of the bookmark: https://itol.embl.de/

Interactive Bioinformatics Resources !

Jit — Thu, 12 Aug 2021 00:09:00 -0500

Learn how to use bioinformatics tools right from your browser.
Everything runs in a sandbox, so you can experiment all you want.

More at sandbox.bio

Address of the bookmark: http://sandbox.bio

S-plot2: creates an interactive, two-dimensional heatmap of sequences

Jit — Fri, 28 Sep 2018 05:36:19 -0500

S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.

http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf

http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot