Please report new whole genome alignment tools under comment sections.

Address of the bookmark: http://www.cs.utoronto.ca/~brudno/721.full.pdf

sort a space-delimited file based on its first column, then the second if the first is the same, and so on:
sort input.txt

sort a huge file (GNU sort ONLY):
sort -S 1500M -t $HOME/tmp input.txt > sorted.txt

sort starting from the third column, skipping the first two columns:
sort +2 input.txt

sort the second column as numbers, descending order; if identical, sort the 3rd as strings, ascending order:
sort -k2,2nr -k3,3 input.txt

sort starting from the 4th character at column 2, as numbers:
sort -k2.4n input.txt

More Linxu sort command information

If you have any sort commands you'd like to share, please add them to our comments section below. For more help, you can also type:

man sort

or

sort --help

on your Unix/Linux system.

https://www.crick.ac.uk/research/labs/charles-swanton

Biostrings

The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. Documentation

The Vicoso group is interested in understanding several aspects of the biology of sex chromosomes, and the evolutionary processes that shape their peculiar features. By combining the use of next-generation sequencing technologies with studies in several model and non-model organisms, they can address a variety of standing questions, such as: Why do some Y chromosomes degenerate while others remain homomorphic, and how does this relate to the extent of sexual dimorphism of the species? What forces drive some species to acquire global dosage compensation of the X, while others only compensate specific genes? What are the frequency and molecular dynamics of sex-chromosome turnover?

More at https://ist.ac.at/en/research/vicoso-group/
http://pub.ist.ac.at/~bvicoso/

Address of the bookmark: http://www.gigasciencejournal.com/content/2/1/15?utm_content=buffer25ee0&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

A new study in Science offers a different and surprisingly sweet perspective. Researchers Jennie Brand-Miller, Karen Hardy, David Raubenheimer, and Les Copeland suggest that sugars from fruits and honey, and later starch from cooked plants, may have been key in helping the human brain evolve.

The brain’s carbohydrate problem

The human brain uses a lot of energy. Even though it is only about 2% of an adult’s body weight, it takes up a large share of the body’s resting energy, mainly using glucose as fuel. Over millions of years, as our ancestors’ brains grew from about 300 grams in Australopithecus afarensis to around 1,500 grams in modern humans, the need for easy-to-access glucose would have gone up a lot.

The researchers, therefore, asked a simple question: where did all that glucose come from?

Their analysis suggests that early hominins may have gotten much of their glucose from naturally sweet foods. Fruit and honey could give them easy access to sugars before they learned to digest cooked starch well. The study’s models show that sugars may have made up a big part of their diet.

From fruit and honey to cooked starch

The story did not end with fruit.

The researchers suggest that humans shifted from eating sweet foods to eating cooked starchy foods. Once people learned to control fire and process food, cooking made starchy plants much easier to digest and turned them into a key source of glucose.

From this perspective, new ways of preparing food, like pounding, processing, and cooking, were not just about making meals softer or tastier. These changes may have given our ancestors access to much more carbohydrate energy.

This gives us a more detailed view of how the human diet evolved. Animal foods were clearly important, but carbohydrates may have been just as important for meeting the high energy needs of a growing brain.

A different way to think about the human diet

The study does not claim that our ancestors ate sugar the way we do today. There is a big difference between eating whole fruits or natural honey and eating the highly processed, refined sugars common now.

Instead, the research points to something bigger. Human evolution may have depended on our ability to find, process, and get energy from many different foods.

Seasonal fruit, honey, underground plant foods, and later cooked starches could have been important sources of glucose. The researchers think that changes in how available these foods were may have shaped how our ancestors searched for food and even affected human evolution.

What does this mean today?

The findings should not be interpreted as a license to eat more refined sugar. Instead, they challenge the simple idea that carbohydrates were less important than meat in human evolution. Our ancestors survived and evolved by exploiting different nutritional opportunities—and by developing technologies that made previously difficult foods more useful.

So perhaps the history of the human brain was not written by meat alone. ISo maybe the story of the human brain is not just about meat. It could also be about fruit, honey, roots, grains, and our unique ability to turn plants into energy. sweeter than we once imagined.

Read more about it at https://www.science.org/doi/epdf/10.1126/science.aed8437

De Bruijn Graphs for NGS Assembly
Algorithms for PacBio Reads
Software and Hardware Concepts for Bioinformatics
Finding us in Homolog.us (Search Algorithms)
NGS Genome and RNAseq Assembly - a Hands on Primer
Introduction to PERL, Python, R and C/C++ for Bioinformatics

Address of the bookmark: http://www.homolog.us/Tutorials/

A recent study published in PLOS Computational Biology introduces REvolutionH-tl 2.0 (https://pypi.org/project/revolutionhtl/), a computational tool designed to help solve this mystery. The software analyzes protein sequences and identifies evolutionary relationships between genes. It can help researchers detect important events such as gene duplication, gene loss, and speciation, allowing them to reconstruct how genes evolved across different organisms.

What makes the tool especially interesting is its focus on both speed and interpretation. The researchers compared REvolutionH-tl 2.0 with several existing computational tools and reported that it achieved competitive accuracy while requiring less computational time in their tests. The tool also includes visualization features, helping researchers turn complicated genetic data into evolutionary stories that are easier to explore.

Genes are more than simple sequences of DNA; they are records of life’s long history. Each surviving gene carries traces of the changes that shaped it. By bringing together computational analysis and visualization, REvolutionH-tl 2.0 acts like a detective, helping scientists uncover these hidden stories. Ultimately, tools like this can improve our understanding of how genomes evolved and how genes acquired the roles they perform today.

More at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013017

August1-2, 2014

Organised By

Centre of Excellence in Bioinformatics
Bioinformatics Infrastructure Facility
Department of Biochemistry
University of Lucknow
Lucknow-226007

Course Contents

Molecular Modeling
Homology Modeling
Molecular Docking
Post-structural Analyses

Molecular Dynamics (MD)
Simulation
Linux Introduction
Gromacs Installation

MD Simulation of Protein ligand complex
Analyses of MD
Trajectories
Visualization of Dynamic
complexes

Important Dates

Registration Begins June 25, 2014
Registration Closes July 25, 2014

Brochure : www.lkouniv.ac.in/conference/Brochure_August,%202014.pdf