BOL: Related items

postdoctoral fellow for a large-scale microbial comparative genomics !

Thu, 29 Apr 2021 08:44:53 -0500

Asaf Levy hiring a postdoctoral fellow for a large-scale microbial comparative genomics project at the Hebrew University of Jerusalem (Israel).
The project is a continuation of Levy Asaf et al. Nature Genetics 2018 paper.
Requirements:
1.Experience with programming in at least one programming language, preferably Python.
2.A PhD in bioinformatics/computational biology
3.At least one first authorship publication in a good journal, preferably more.
4.Good communication skills in English
5.Ability to enter and study in Israel (not applicable for Pakistani people, for example).
6.Ability to work in a team.
Please send CV to alevy@mail.huji.ac.il

Bioinformaticians in comparative and evolutionary genomics

Tue, 02 Aug 2022 01:22:48 -0500

NBIS is now looking for a new member to support Swedish research in evolutionary, comparative, and population genomics, with a particular focus on conifer genomics.

Your tasks will consist of:

Advanced bioinformatics analyses within research projects across Sweden, including key involvement in a major research effort in conifer genomics.
Development of bioinformatics tools and workflows.
Educating other scientists in bioinformatics through collaboration within supported projects, teaching at national courses, and through participating in various networks.
Taking part in the continuous development of NBIS/SciLifeLab at a national level

More at https://www.uu.se/en/about-uu/join-us/details/?positionId=518909

Unlocking Evolutionary Secrets: A Dive into Comparative Genomics Methods

LEGE — Tue, 20 May 2025 00:25:09 -0500

Comparative genomics is the art and science of comparing genomes—across species, within species, or even among individuals—to unravel evolutionary relationships, functional elements, and genetic adaptations. As sequencing technologies have advanced and genome databases have expanded, comparative genomics has become a cornerstone of modern biology, shedding light on everything from antibiotic resistance in bacteria to human disease genetics.

In this post, we’ll explore the core methods used in comparative genomics, the questions they help answer, and how they’re shaping our understanding of life.

1. Whole-Genome Alignment
Whole-genome alignment involves mapping the entire genome of one species to another. Tools like MUMmer, MAUVE, and LASTZ perform large-scale sequence alignments to detect conserved regions, rearrangements, insertions, and deletions.

Use Case:
Comparing human and chimpanzee genomes to identify evolutionary conserved sequences (ECS) and regions of divergence.

Key Challenges:
Handling repetitive sequences and genome rearrangements.

Computational complexity in large genomes.

2. Synteny and Collinearity Analysis
Synteny refers to conserved blocks of gene order across species. Tools like MCScanX, SynMap, or CHITRA (for visualizing synteny interactively) detect these blocks to understand chromosomal evolution.

Use Case:
Studying ancient genome duplications in plants.

Investigating chromosomal rearrangements in cancer genomes.

3. Ortholog and Paralog Detection
Orthologs are genes in different species that evolved from a common ancestor, while paralogs are genes duplicated within a genome. Identifying them is crucial for functional annotation and evolutionary studies.

Popular Tools:
OrthoFinder, Orthologous MAtrix (OMA), InParanoid, and EggNOG.

Use Case:
Functional prediction of uncharacterized genes based on orthologs in model organisms.

Tracing gene family evolution.

4. Phylogenomic Analysis
Phylogenomic methods combine phylogenetics and genomics to infer evolutionary trees based on genome-wide data. These methods can handle dozens to hundreds of genomes, using concatenated alignments or gene trees.

Tools:
RAxML, IQ-TREE, ASTRAL, Phylip, BEAST.

Use Case:
Resolving the evolutionary relationships between microbial species.

Studying speciation events.

5. Pan-Genome Analysis
The pan-genome consists of the core genome (shared by all strains) and the accessory genome (strain-specific genes). This is especially popular in microbial genomics.

Tools:
Roary, Panaroo, BPGA, PGAP.

Use Case:
Understanding virulence factor diversity in E. coli.

Designing broad-spectrum vaccines.

6. Comparative Transcriptomics
Comparing transcriptomes across species or conditions reveals conserved and unique expression patterns. RNA-seq data can be mapped to reference genomes to identify orthologous expression profiles.

Use Case:
Comparing stress response in extremophiles and model species.

Studying conserved regulatory networks.

7. Functional Element Comparison
Beyond genes, comparative genomics also targets non-coding regions—enhancers, promoters, miRNAs. Conservation across species often implies functional importance.

Tools:
PhastCons, GERP, phyloP (based on multiple alignments).

Use Case:
Detecting conserved non-coding elements in vertebrates.

Studying regulatory divergence in human evolution.

8. Horizontal Gene Transfer (HGT) Detection
In microbes, genes often jump across species boundaries. Comparative genomics can detect HGT by identifying genes that defy the expected phylogenetic pattern.

Tools:
HGTector, DarkHorse, AlienHunter, SIGI-HMM.

Use Case:
Tracing antibiotic resistance genes.

Exploring microbial adaptability in extreme environments.

Final Thoughts
Comparative genomics is a powerful lens to observe the diversity and unity of life. With a broad toolkit—from aligners to orthology pipelines, phylogenetic engines to visualization tools—it allows scientists to ask big questions: How did genomes evolve? What makes species unique? Where do new genes come from?

Whether you're studying extremophiles, building better crops, or exploring human ancestry, comparative genomics offers the methods to connect the dots across the tree of life.

Learning Python Programming - a bioinformatician perspective !

Rahul Nayak — Mon, 14 May 2018 16:33:03 -0500

Python Programming is a general purpose programming language that is open source, flexible, powerful and easy to use. One of the most important features of python is its rich set of utilities and libraries for data processing and analytics tasks. In the current era of big biological data, python and biopython is getting more popularity due to its easy-to-use features which supports big data processing.

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

It's widely used in the scientific community
It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
It lend itself well to being integrated with other, existing tools
It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
Python is dynamically typed, so the variables types are defined automatically.
Python is strongly typed. So the developers need to cast the type manually.
Less code and more use makes it more acceptable.
Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.

GLEAN: an unsupervised learning system to integrate disparate sources of gene structure evidence

Poonam Mahapatra — Sat, 02 Jun 2018 07:38:33 -0500

GLEAN is an unsupervised learning system to integrate disparate sources of gene structure evidence (gene model predictions, EST/protein genomic sequence alignments, SAGE/peptide tags, etc) to produce a consensus gene prediction, without prior training.

Address of the bookmark: https://sourceforge.net/projects/glean-gene/

A guide to machine learning for biologists

LEGE — Wed, 15 Sep 2021 13:21:08 -0500

We aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

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

Papenfuss Lab

Sun, 14 Jul 2013 12:22:28 -0500

The human genome project and similar projects in disease-causing organisms such as Plasmodium falciparum, which causes malaria in humans, have provided new tools for discovery in biology and have accelerated the development of understanding in human disease.

Research Area:
Analysis of Next Generation sequence data in cancer
Methods for analysis of structural variation in cancer genomes
Next Generation sequencing in malaria
Computational comparative genomics
Sensitive genomic sequence search techniques using hidden Markov models
Tasmanian devil facial tumour disease

Link @ http://www.wehi.edu.au/faculty_members/dr_tony_papenfuss

Bioinformatics PhD studentship available in New Zealand

Sun, 14 Jul 2013 13:36:30 -0500

Bioinformatics PhD studentship available in New Zealand

The importance of transcriptional control has been explored in a burgeoning line of research over several decades; nevertheless, we are still far from having a complete picture of the regulatory mechanisms of genes and non-coding RNAs, and their influences on different phenotypes and disease states of a cell. Recent shifts towards large-scale analyses of transcriptional regulation on a sequence and epigenetic level are at the forefront of research, mainly due to sequencing technology advancements and a deeper understanding of the fundamental regulatory processes involved.

Arriving at a better understanding of the influence of specific parts of the overall regulatory machinery on disease states is a high priority of the group’s research agenda.

We are seeking an enthusiastic student to join the group as a PhD student. Applicants must have a BSc(Hons) or MSc degree in a relevant discipline and a willingness to learn and apply new techniques and work in a team. Both local and international students are encouraged to apply.

The studentship covers all university fees and an annual tax-exempt stipend of NZ$22,000 for three years.

Sebastian Schmeier recently joined Massey University and started his own research group in Auckland, New Zealand, a city regularly ranked one of the most livable in the world. This is your chance to experience the amazing Auckland lifestyle and the excitement of joining a young new science team, while staying connected to world class scientific networks.

To apply for the post, please send a cover letter stating your interest in the position and why you think you would be a good candidate, a Curriculum Vitae, a copy of your academic transcript, a sample of your written scientific work, and the names of three referees. Applications will be accepted until the position is filled.

Enquiries and applications to Sebastian Schmeier (s.schmeier@massey.ac.nz).

Phylogenetic for Bioinformatics

Jitendra Narayan — Tue, 16 Jul 2013 03:50:30 -0500

Biologists estimate that there are about 5 to 100 million species of organisms living on Earth today. Evidence from morphological, biochemical, and gene sequence data suggests that all organisms on Earth are genetically related, and the genealogical relationships of living things can be represented by a vast evolutionary tree, the Tree of Life. The Tree of Life then represents the phylogeny of organisms, i. e., the history of organismal lineages as they change through time.
Every living organism contains DNA, RNA, and proteins. Closely related organisms generally have a high degree of agreement in the molecular structure of these substances, while the molecules of organisms distantly related usually show a pattern of dissimilarity. Molecular phylogeny uses such data to build a "relationship tree" that shows the probable evolution of various organisms. Not until recent decades, however, has it been possible to isolate and identify these molecular structures.
phylogenetics is the study of evolutionary relatedness among various groups of organisms (for example, species or populations), which is discovered through molecular sequencing data and morphological data matrices. In other word, Phylogenetics, the science of phylogeny, is one part of the larger field of systematics, which also includes taxonomy. Taxonomy is the science of naming and classifying the diversity of organisms Molecular phylogeny is the use of the structure of molecules to gain information on an organism's evolutionary relationships. The result of a molecular phylogenetic analysis is expressed in a so-called phylogenetic tree.

The evolutionary connections between organisms are represented graphically through phylogenetic trees. Due to the fact that evolution takes place over long periods of time that cannot be observed directly, biologists must reconstruct phylogenies by inferring the evolutionary relationships among present-day organisms.
Application of the techniques that make this possible can be seen in the very limited field of human genetics, such as the ever more popular use of genetic testing to determine a child's paternity, as well as the emergence of a new branch of criminal forensics focused on genetic evidence.
The effect on traditional scientific classification schemes in the biological sciences has been dramatic as well. Work that was once immensely labor- and materials-intensive can now be done quickly and easily, leading to yet another source of information becoming available for systematic and taxonomic appraisal. This particular kind of data has become so popular that taxonomical schemes based solely on molecular data may be encountered. Proponents even claim that taxonomy was previously based on morphology alone, which of course is utter fable.

For additional information on phylogenetics, see list of Phylogenetics Resources on the Internet.

Phylogeny and Reconstructing Phylogenetic Trees: http://aleph0.clarku.edu/~djoyce/java/Phyltree/cover.html
the CBRG and Department of Statistics Phylogeny tutorial: http://www.compbio.ox.ac.uk/tutorials/phylogeny/
TUTORIAL: PHYLOGENETIC ANALYSIS USING PARSIMONY:http://home.cc.umanitoba.ca/~psgendb/GDE/phylogeny/parsimony/phylip.parsimony.html

PHYLIP: http://www.umanitoba.ca/afs/plant_science/psgendb/doc/Phylip/main.html
An Introduction to Molecular Phylogeny: http://bibiserv.techfak.uni-bielefeld.de/gcb04/tutorials/hoef-emden/GCB04Tut.pdf

How to make a phylogenetic tree: http://www.hiv.lanl.gov/content/sequence/TUTORIALS/TREE_TUTORIAL/Treetutorial.html
Phylogenetic Trees: http://cnx.org/content/m11052/latest/
Phylogeny by Ron Shamir: http://www.cs.tau.ac.il/~rshamir/algmb/01/scribe08/lec08.pdf
Introduction to Phylogeny: http://www.utm.edu/departments/cens/biology/rirwin/391/391Phylog.htm
Lecturer notes on Phylogeny: http://www.sbc.su.se/~bens/course_material/phylocourse1/lecture2.pdf
Principles and Practice of Phylogenetic Systematics:http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/LectureNotes.htm

Inferring phylogenetic trees: http://www.cis.hut.fi/Opinnot/T-61.6070/slides2008/pres_6070.pdf

Lecture Notes

Chapter 1 - The Diversity, Classification, and Evolution of Vertebrates:http://academic.emporia.edu/mooredwi/nathist/chap1.htm

Algorithms for Phylogenetic Reconstructions:http://lectures.molgen.mpg.de/Algorithmische_Bioinformatik_WS0405/phylogeny_script.pdf

Phylogeny.fr is a free, simple to use web service dedicated to reconstructing and analysing phylogenetic relationships between molecular sequences. Phylogeny.fr runs and connects various bioinformatics programs to reconstruct a robust phylogenetic tree from a set of sequences. For more detail : http://www.phylogeny.fr/version2_cgi/index.cgi

A Brief Tutorial on Phylogenetics
http://bioss.ac.uk/~dirk/talks/tutorial_phylogenetics.pdf

A Brief Tutorial on Phylogenetics Human Rabbit Chicken
http://bioss.ac.uk/~dirk/talks/psnup_tutorial_phylogenetics.pdf

Phylogenetic Tree Computation Tutorial Overview
http://pga.lbl.gov/Workshop/April2002/lectures/Olken.pdf

MrBayes: A program for the Bayesian inference of phylogeny
http://golab.unl.edu/teaching/SBseminar/manual.pdf

Web sites providing software for the construction of phylogenetic trees