MALVA calls correctly more indels than the most widely adopted genotyping pipelines

Mapping-free approaches are as accurate as alignment-based ones, while being faster

More at https://www.sciencedirect.com/science/article/pii/S2589004219302366

https://www.sciencedirect.com/science/article/pii/S2589004219302366

Address of the bookmark: https://github.com/AlgoLab/malva

Address of the bookmark: https://github.com/meiji-bioinf/heap

Address of the bookmark: https://github.com/walaj/svaba

• multi-class semantic segmentation using U-Net architecture
• multi-class object detection using YOLOv3 architecture

You can install the latest version of platypus with remotes package:

Note that in order to install platypus you need to install keras and tensorflow packages and Tensorflow version >= 2.0.0 ( Tensorflow 1.x will not be supported!)

Address of the bookmark: https://github.com/maju116/platypus

It is enormous, billions of letters long. Some of those letters are easy to understand because they contain instructions for building proteins, the tiny machines that keep our cells alive.

But what if some of the most important instructions aren't in those obvious chapters?

Scientists have long suspected that the mysterious stretches of DNA between protein-coding genes may contain some of life's most interesting secrets. These regions can act like switches, telling genes when, where, and how strongly to turn on. Among them are conserved non-coding elements, or CNEs DNA sequences that have remained remarkably similar across different species.

Think of a CNE as a sentence that nature has copied into several editions of the same book.

A mouse has it.

A bird has it.

A human has it.

And despite millions of years of evolution, much of the sentence remains almost unchanged.

That conservation is a clue: this piece of DNA probably matters.

But there was a problem. Finding these hidden sentences across enormous genomes is difficult. Researchers needed to compare many genomes, identify conserved regions, and then determine which of those regions might have evolved unusually quickly in particular branches of the evolutionary tree.

Enter CNEwrap. https://github.com/YanCCscu/CNEwrap

The researchers developed CNEwrap as a streamlined toolkit capable of handling large-scale genome comparisons, conserved-element detection, and evolutionary analysis. Inside it is a new algorithm called EvoAcc, designed to identify CNEs that show accelerated evolution in specific species or lineages.

Why does that matter?

Because evolution isn't just about what stays the same.

Sometimes, the most interesting story is hidden in what changed.

Imagine that nearly every species carries the same regulatory sentence, but one lineage suddenly has several unusual edits. Perhaps those changes helped shape a new body structure, a different adaptation, or another distinctive trait.

EvoAcc is designed to help researchers find these evolutionary clues. In the study's tests, it performed particularly well in scenarios involving two or three accelerated lineages and showed improved sensitivity to insertion and deletion mutations compared with several existing approaches.

The researchers also tested CNEwrap using functional genomic regions from mammals and found that EvoAcc could recover human-specific accelerated regions while identifying some signals that other methods missed.

So the story of CNEwrap is really a story about looking beyond the obvious.

For decades, scientists have often focused on the DNA that directly encodes proteins. But the genome is more like a vast library: the protein-coding genes are the main text, while non-coding regions may contain punctuation, instructions, switches, annotations, and editing notes that tell the cell how to read the book.

CNEwrap gives researchers a new way to search that hidden material.

And perhaps somewhere inside those billions of DNA letters is an evolutionary story we haven't read yet.

The exciting part is that we now have better tools to find it.

Read more about it https://academic.oup.com/nar/article/54/14/gkag709/8738333?

The project is on bacterial gene gain and loss and emerging pathogenicity, and is described in full here: https://www.ebi.ac.uk/research/postdocs/ebpods/projects/goldman-welch-2015 . The EMBL-EBI / Cambridge Computational Biomedical Postdoctoral (“EBPOD”)

The closing date for applications is 3 September 2015. Nick Goldman EMBL-European Bioinformatics Institute Nick Goldman

More at https://www.ebi.ac.uk/research/postdocs/ebpods/projects/goldman-welch-2015

The fate of a cell is ultimately the product of the regulation of its genes. Gene regulation is a coordinated process acting at multiple levels of which transcription and translation are the most prominent. The Valen group is dedicated to the fundamental question of how transcription and translation is integrated to obtain the desired protein abundance. The recent development of high-throughput next generation sequencing techniques to monitor both active translation and transcription has made it possible to study this connection at the genome scale.

This project aims to elucidate the links between regulation of translation and transcription. The applicant will analyze next generation sequencing data and model gene regulation on a genome-wide level to identify the features that affect the translational output of transcripts. The work will be done in close collaboration with experimental scientists who will test the predictions of the computational models.

Additional information on the position can be obtained by contacting Eivind Valen (eivind.valen@ii.uib.no).

The research fellow must take part in the University’s approved PhD program leading to the degree within a time limit of 3 years. Application for admission to the PhD program, including a project plan outline for the training module, will be worked out in collaboration with the research group in question.

In total, the fellowship period is 4 years, 25 % of this will be allocated to teaching and/or administrative duties. The fellowship period may be reduced if the successful applicant has held previous employment as a research fellow or similar.

http://www.jobbnorge.no/en/available-jobs/job/102235/research-fellow-phd-candidate-in-computational-biology-2-positions

About 8,000 years ago, the gene EGLN1 changed by a single DNA base pair. Today, a relatively short time later on the scale of human history, 88 percent of Tibetans have the genetic variation, and it was virtually absent from closely related lowland Asians. The findings indicate the genetic variation endows its carriers with an advantage.

In those without the adaptation, low oxygen caused their blood to become thick with oxygen-carrying red blood cells, an attempt to feed starved tissues, which could cause long-term complications such as heart failure. The researchers found that the newly identified genetic variation protected Tibetans by decreasing the over-response to low oxygen.

Reference: http://www.nature.com/nature/journal/v512/n7513/abs/nature13408.html

http://katjulab.com/research.html

If you would like to use GeneValidator on a few sequences, see our online GeneValidator Web App -http://genevalidator.sbcs.qmul.ac.uk.

If you use GeneValidator in your work, please cite us as follows:

Dragan M‡, Moghul MI‡, Priyam A, Bustos C & Wurm Y. 2016. GeneValidator: identify problems with protein-coding gene predictions. Bioinformatics, doi: 10.1093/bioinformatics/btw015.

 

Address of the bookmark: https://github.com/wurmlab/genevalidator