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

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

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

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

There are Neanderthal bones. Denisovan remains. Fossils from ancient relatives of our species. Each specimen gives us another piece of the story of how Homo sapiens came to be.

But now imagine that one of the most important characters in that story has left behind no fossil, no skull, no bone, and no genome.

Nothing that a paleontologist could put behind glass.

And yet, somehow, that ancient population is still speaking.

It is speaking through us.

A fascinating new study published in Science (https://www.science.org/doi/10.1126/science.aef8874?) introduces a computational method called TRACE, which allows scientists to search modern human genomes for genetic traces of ancient populations that have never been directly identified.

For years, scientists have known that our ancestors interacted with Neanderthals and Denisovans. Their DNA still survives in modern human populations. But these may not have been the only ancient humans who crossed paths with our ancestors.

Using TRACE, researchers found evidence of an unknown archaic population that contributed DNA to the ancestors of modern humans before humans spread beyond Africa.

Even more surprisingly, they found evidence of a much older lineage—possibly around 1.8 million years old—whose genetic legacy may have reached some modern humans indirectly through Denisovans.

The extraordinary part is that researchers did not need DNA from these mysterious populations to detect them. Instead, they studied the ancestry of modern genomes and looked for genetic patterns that revealed unusually ancient branches of our family history.

It is almost like finding the footprints of someone who disappeared thousands of years ago—without ever finding the person themselves.

These discoveries suggest that human evolution was far more complicated than a simple family tree. Ancient human populations repeatedly separated, migrated, met and exchanged genes. Some eventually disappeared, but pieces of their genetic legacy survived.

And that means our DNA is more than a biological instruction manual.

It is also an archive of human history.

Some of the people recorded in that archive may never have a name or a fossil. But thanks to methods like TRACE, their genetic echoes are finally becoming visible.

The next big discovery about our ancient past may not come from a fossil in the ground.

It may already be inside us.

Reference: Zhang et al., “Recovering signatures of archaic hominin introgression using ancestral recombination graphs,” Science (2026), DOI: 10.1126/science.aef8874. Detail paper @ https://www.science.org/doi/10.1126/science.aef8874?

The TRACE repository (https://github.com/YulinZhang9806/trace) contains the Python package and command-line tools used to infer archaic admixture tracts from ancestral recombination graphs.

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

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

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

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/

More at sandbox.bio

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