BOL: Related items

Bioinformatics Scientist, Production Bioinformatics @ South San Francisco, CA

Thu, 19 Aug 2021 08:45:24 -0500

wist is looking for a Bioinformatics Scientist to join our Production Bioinformatics Team. You will work alongside research scientists, software engineers and data scientists to further deliver on our mission to expand access to best-in-class synthetic biology and next-generation sequencing applications. You will be developing and engineering tools to better evaluate and build hardened, production quality pipelines, optimize data quality, and automate lab and bioinformatics processes. Our ideal candidate is an organized problem solver with a background in developing and building novel production-quality bioinformatics tools and packages. Equally excellent communication skills and a proven ability to work independently are required.

More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us

Bioinformatics Training Material !

BioStar — Sat, 18 Mar 2023 11:26:18 -0500

Glittr is a curated list of bioinformatics training material.
All material is:

In a GitHub or GitLab repository
Free to use
Written in markdown or similar

NOTE: This list of courses is selected only based on the above criteria.
There are no checks on quality.

https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Address of the bookmark: https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Online resources on must-read papers in evolutionary biology

BioStar — Fri, 26 Jul 2024 01:39:14 -0500

Online resources on must-read papers in evolutionary biology, for a literature club.

Below is a summary of all answers that we received.

All the best,

Jana and Xiaoyan

1.       *Nick Barton:*

- The textbook "Evolution" by Nick Barton, with resources for
  exploring the literature: Barton, N. H., Briggs, D. E. G., Eisen, J.
  A., Goldstein, D. B., & Patel, N. H. (2007). Evolution. Cold Spring
  Harbor Laboratory Press.

- Papers from a course named "Classics in Evolutionary Biology":

Evolutionary Synthesis
1. Haldane, J. B. S. 1932. The causes of evolution. Longmans. New York.
   (esp. Ch. IV).
2. Fisher, R. A. 1930. The genetical theory of natural selection. Oxford
   University Press, Oxford. Selected Sections - Fundamental Theorem.

Genetic Variation
1a. Lewontin, R. C., and J. L. Hubby. 1966. A molecular approach to
the study of genic heterozygosity in natural populations. II. Amount
of variation and degree of heterozygosity in natural populations of
Drosophila pseudoobscura. Genetics. 54:595-609.

1b. Sachidandam et al. 2001. A map of human genome sequence variation
containing 1.42 million single nucleotide polymorphisms. 409: 928-33.

2. Wright S., Dobzhansky T., Hovanitz W. 1942 Genetics of natural
populations VII The allelism of lethals in the third chromosome of
Drosophila pseudoobscura. Genetics 27: 363-394.

Recombination and evolution
1. Hill, W. G., and A. Robertson. 1966. The effect of linkage on limits
to artificial selection. Genet. Res. 8:269-294.

2. Maynard Smith and Haigh. 1974. The hitch-hiking effect of a favourable
gene. Genet. Res. 23: 23-35.

Understanding sequence variation
1. Begun D. J., Aquadro C. F., 1992 Levels of naturally occurring DNA
polymorphism correlate with recombination rate in Drosophila melanogaster.
Nature 356: 519-520.

2. Green R. E., Reich D., Pääbo S., 2010 A draft sequence of the
Neandertal genome. Science 328: 710-722.

Quantitative Genetics:  variation in complex traits
1. Galton F., 1877 Typical laws of heredity. Nature 15: 492-495-
512-514- 532-533.

2. Turelli M., 1984 Heritable genetic variation via
mutation-selection balance: Lerch's Zeta meets the abdominal
bristle. Theor. Popul. Biol. 25: 138-193.

Quantitative Genetics:  finding the genes
1. Shrimpton A. E., Robertson A., 1988 The Isolation of polygenic factors
controlling bristle score in Drosophila melanogaster II Distribution of
third chromosome bristle effects within chromosome sections. Genetics
118: 445-459.

2. Boyle E. A., Li Y. I., Pritchard J. K., 2017 An expanded view of
complex traits: from polygenic to omnigenic. Cell 169: 1177-1186.

Neutral Evolution
1. Kimura, M. 1968. Evolutionary rate at the molecular level. Science.
217:624-626.

2a. Kern A. D., Hahn M. W., 2018 The Neutral Theory in Light of Natural
Selection. Molecular Biology and Evolution 110: 21077-6.

2b. Jensen J. D., Payseur B. A., Stephan W., Aquadro C. F., Lynch M.,
Charlesworth D., Charlesworth B., 2018 The importance of the Neutral Theory
in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution 112:
2109-4.

2c. Ellegren & Galtier. 2016. Determinants of genetic diversity. Nature
Reviews Genetics.

Mutation and Genetic Variability
1. Luria, S. E., and M. Delbrück. 1943. Mutations of Bacteria from Virus
Sensitivity to Virus Resistance. Genetics. 28(6):491-511.

2. Hill, W G. 1982. "Rates of Change in Quantitative Traits From Fixation
of New Mutations." Proceedings of the National Academy of Sciences (U.S.A.)
79: 142-45.

Testing for selection
1. McDonald & Kreitman. 1991. Adaptive protein evolution at the Adh locus
in Drosophila. Nature.

2. Begun, et al. Mol. Biol. Evol. 16, 1816-1819 (1999).

3. Siddiq et al. 2016. Experimental test and refutation of a classic case
of molecular adaptation in Drosophila melanogaster.  Nature Ecology &
Evolution.

The shifting balance
1. Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and
selection in evolution. Proceedings of the VI International Congress of
Genetics: 1. pp 356-366.

2. Coyne, J.A., N.H. Barton, and M. Turelli. 1997. A critique of Wright's
shifting balance theory of evolution.  Evolution 51: 643-671.

3. Barton. 2016. Sewall Wright on Evolution in Mendelian Populations and
the "Shifting Balance". Genetics.

Evolution of Sex
1.  Muller, H.J. 1964. The relation of recombination to mutational advance.
Mutation Res. 1(1):2-9

2. McDonald et al. 2016. Sex speeds adaptation by altering the dynamics of
molecular evolution. Nature.

Kin Selection, Cooperation, and Conflict
1. Hamilton, W. D. 1964. The genetical evolution of social behaviour I.
Journal of Theoretical Biology. 7:1-52.

2. Trivers, R. L. 1974 Parent-offspring conflict. American Zoologist.
14(1):249-264.

Sexual Selection
1. Zahavi, A. 1975. Mate selection - a selection of a handicap. J. Theor.
Biol. 53:205-214.

2. Kirkpatrick, M., and Ryan, M.J. 1991. The evolution of mating
preferences and the paradox of the lek. Nature. 350:33-38.

Fitness Landscapes
1. Dean, A. 1995. A Molecular Investigation of Genotype by Environment
Interactions. Genetics. 139:19-33.

2. Costanzo et al. 2010. The Genetic Landscape of a Cell. Science.

Speciation
1. Coyne, J. A., and H. A. Orr. 1989. Patterns of speciation in Drosophila.
Evolution. 43:362-381.

2. Corbett-Detig et al. 2013. Genetic incompatibilities are widespread
within species. Nature.

2.       *Marcos Antezana:*

Valen, L. v. 1975. Energy and Evolution. University of Chicago, Department
of Biology.

3.       *Remco Folkertsma:*

1. The work by Hopi Hoekstra on local adaptation and oldfield mice

2. Poelstra, J. W., Vijay, N., Bossu, C. M., Lantz, H., Ryll, B., Müller,
I., ... & Wolf, J. B. (2014). The genomic landscape underlying phenotypic
integrity in the face of gene flow in crows. Science, 344(6190), 1410-1414.

4.       *Joshka Kaufmann and Leslie Turner*

They offer us a link to 'papers every evolutionary biologist should read',
the papers are collected by Leslie Turner.
https://static1.squarespace.com/static/53e8cb7ce4b02c4bc3aeeee4/t/5ab8fcb670a6ad55c67fcdf4/1522072758665/EvoBioClassicsRefList.pdf

5.       *Sarah Stockwell*

Matt Ridley collected classic papers in evolutionary biology and printed
part of these papers in his book Evolution (see Matt Ridley. Evolution
(Univ. of Oxford Press, 2nd edition, 2004))

Machine Learning !!!

Gudiya Pal — Fri, 01 Jul 2016 12:57:12 -0500

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Statistical for biological research

Neel — Thu, 03 Nov 2016 04:59:48 -0500

There is no disputing the importance of statistical analysis in biological research, but too often it is considered only after an experiment is completed, when it may be too late.

This collection highlights important statistical issues that biologists should be aware of and provides practical advice to help them improve the rigor of their work.

Nature Methods' Points of Significance column on statistics explains many key statistical and experimental design concepts. Other resources include an online plotting tool and links to statistics guides from other publishers.

Address of the bookmark: http://www.nature.com/collections/qghhqm

NGS teaching material

Jit — Wed, 05 Apr 2017 04:29:06 -0500

High throughput sequencing (HTS) technologies are being applied to a wide range of important topics in biology. However, the analyses of non-model organisms, for which little previous sequence information is available, pose specific problems. This course addresses the specific strengths and weaknesses of alternative HTS technologies, the computational resources needed for HTS, and how to analyze non-model species using HTS. The course consists of a practical training module, HTS bioinformatics training, and lecturing/seminars of HTS approaches specifically targeting non-model organisms.

Address of the bookmark: http://marinetics.org/teaching/hts/Assembly.html

Bioinformatics in Africa: Part5 - Nigeria

BioStar — Sat, 06 Feb 2021 21:13:47 -0600

Covenant University (CU)Ota:
Covenant University (with her enriching and growing stateoftheart laboratories in the area of science and technology, arts, business and social sciences) is presently the Best University in Nigeria (Private University category), based on the recent overall rating just concluded by the Nigeria University Commission (NUC). Recently, Covenant University has initiated the establishment of a Centre for Applied Biotech, BioInformatics and Microbiology (CBBM) to be situated at the University. The institute has been designed to be a PublicPrivate Partnership for a productive synergy b/w Academia, Industry and Government. The whole concept is still evolving and more details will be release soon. As regards CBBM, a dedicated computing lab is in plan, but even our computing capacity is presently enormous. In the department of Computer and Information Sciences, we have more than 250 Pentium 4 PCs set aside for teaching and research purposes. Furthermore, we have several moderate speed PCs at the Postgraduate research lab and our engineering departments and units. Our wet lab facilities is presently minimal (basic for teaching), the Centre requirement as it touches the wetlaboratories is also set to upgrade this to basic tools expected at an international centre of learning.

University of Ibadan (UIB)Ibadan:
There has been significant increase in the number of bioinformatics activities in Nigeria (and West Africa) since 2003 when the program was initiated by the West African Biotechnology Workshops Series (WABWS, http://www.wabw.org) at the University of Ibadan, Nigeria (in collaboration with the South African National Bioinformatics Institute (SANBI, http:/www.sanbi.ac.za). Workshops that were open to scientists from all African countries have seen a very high number of applications from scientists based in West Africa. The encouraging desire to acquire cuttingedge skills to computational process data and extract useful knowledge from genome projects led to the interest of the West African Biotechnology Workshops (WABW) to develop an agenda to address the bioinformatics skills gap among scientists in West Africa. An increased commitment from agencies like NEPAD would be required in the provision of infrastructure to establish and sustain regional and national networks.

University of Ilorin (UIL)Ilorin:
The University of Ilorin was established in 1976 by the Federal Government of Nigeria. Bioinformatics activities started at the University in February 2003 with the establishment of the West African Bioinformatics Research Initiative (WABRI). However, progress has been rather slow due to inadequate funding. We are mainly engaged in Bioinformatics training at the introductory level and proteomics studies on various species of malaria parasites. Recently, we became interested in comparative genome analysis of various species of Plasmodium and the comparison of chloroquine sensitive and chloroquine resistant strains of Plasmodium falciparum. Other activities and areas of interest can be seen on our website, http://www.wabri.org, although not all our proposed interests have been fully implemented due to our level of funding.

Training:
The University of Ilorin has introduced M.Sc. and Ph.D. programmes in Computer Science (with options in Bioinformatics). The programme is based in the Department of Computer Science and emphasis is on the development of algorithms to solve problems in bioinformatics. The Covenant University offers M.Sc. and Ph.D in Computer Science with option in Bioinformatics (Computational Biology). Furthermore, through affiliated departments, the CBBM is been design to award Diploma and Degree certificates in Biotechnology.

Web sites and links: http://www.covenantuniversity.com http://www.run.edu.ng http://www.uniben.edu http://www.wabri.org http://www.wabw.org http://www.unilorin.edu.ng http://www.wabri.org http://www.asopah.org

10 Books to Kickstart (and Level Up) Your Bioinformatics Journey

Neel — Tue, 12 Aug 2025 03:50:11 -0500

If you’re starting out in bioinformatics or looking to sharpen your computational biology skills, having the right learning resources makes all the difference.
Here’s my curated list of 10 must-read books — from beginner-friendly introductions to advanced computational genomics.

1️⃣ Data Analysis for the Life Sciences
A fantastic starting point to learn statistics, R programming, and exploratory data analysis in the context of biology. The best part? It’s available free online from HarvardX.

2️⃣ Practical Computing for Biologists
The very first book I picked up when I started learning computational biology. It’s beginner-friendly and focuses on essential computing skills every biologist needs.

3️⃣ A Primer for Computational Biology
An open-access, hands-on introduction to computational biology concepts and coding techniques. Perfect if you want to learn through real examples.

4️⃣ Computational Genomics with R
For those who already know R and want to dive deeper into genome-scale data analysis, from sequence alignment to gene expression.

5️⃣ The Biologist’s Guide to Computing
Bridges the gap between biological problems and computational thinking, making it easier for life scientists to approach programming and data analysis.

6️⃣ Bioinformatics Data Skills
A must-read to sharpen your bioinformatics toolkit — from command-line skills to reproducible research workflows. Ideal once you’ve covered the basics.

7️⃣ Bioinformatics Workbook
A practical tutorial series to help scientists design bioinformatics projects, analyze data, and understand best practices.

8️⃣ Modern Statistics for Modern Biology
An essential guide to modern statistical methods applied to biology, blending theory with hands-on examples in R.

9️⃣ Algorithms on Strings, Trees, and Sequences by Dan Gusfield
A classic reference for anyone wanting to understand the algorithms behind sequence alignment, genome assembly, and biological data structures.

Python learning resources for bioinformatics and computational biologist !

Jit — Fri, 02 Mar 2018 06:54:15 -0600

Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

20 Python Libraries You Aren't Using (But Should) (Just fill the fields with any values)
A Beginner's Python Tutorial
A Byte of Python (3.x) (HTML, PDF, EPUB, Mobi)
A Guide to Python's Magic Methods - Rafe Kettler
A Whirlwind Tour of Python - Jake VanderPlas (PDF) (EPUB, MOBI)
Automate the Boring Stuff - Al Sweigart
Biopython (PDF)
Build applications in Python the antitextbook (3.x) (HTML, PDF, EPUB, Mobi)
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho, Packt. (Just fill the fields with any values)
Building Skills in Object-Oriented Design (Python) (PDF) (2.1.1)
Building Skills in Python (PDF) (2.6)
Code Like a Pythonista: Idiomatic Python
CodeCademy Python
Composing Programs (3.x)
Data Structures and Algorithms in Python - B. R. Preiss (PDF)
Dive into Python 3 - Mark Pilgrim (3.0)
- Dive into Python - Mark Pilgrim (2.3)
From Python to NumPy
Full Stack Python
Functional Programming in Python (Just fill the fields with any values)
Fundamentals of Python Programming - Richard L. Halterman (PDF) (3.2)
Google's Python Class (2.4 - 2.x)
Google's Python Style Guide
Hacking Secret Cyphers with Python - Al Sweigart (3.3)
Hadoop with Python (Just fill the fields with any values)
High Performance Python (PDF)
Hitchhiker's Guide to Python! (2.6)
How to Make Mistakes in Python - Mike Pirnat (PDF) (1st edition)
How to Think Like a Computer Scientist: Learning with Python, Interactive Edition (3.2)
- How to Think Like a Computer Scientist: Learning with Python - Allen B. Downey, Jeff Elkner and Chris Meyers (2.4)
- Think Python - Allen B. Downey (2.x & 3.0)
Intermediate Python - Muhammad Yasoob Ullah Khalid (1st edition)
Introduction to Programming with Python (3.3)
- Introduction to Programming Using Python - Cody Jackson (1st edition) (2.3)
Introduction to Python - Kracekumar (2.7.3)
Invent Your Own Computer Games With Python - Al Sweigart (3.1)
Learn Python, Break Python
Learn Python in Y minutes
Learn Python The Hard Way (2.5 - 2.6)
Learn to Program Using Python - Cody Jackson (PDF)
Learning Python - Fabrizio Romano, Packt. (Just fill the fields with any values)
Learning to Program
Lectures on scientific computing with python - J.R. Johansson (2.7)
Making Games with Python & Pygame - Al Sweigart (2.7)
Modeling Creativity: Case Studies in Python - Tom D. De Smedt (PDF)
Natural Language Processing with Python (3.x)
Non-Programmer's Tutorial for Python 3 (3.3)
- Non-Programmer's Tutorial for Python 2.6 (2.6)
Picking a Python Version: A Manifesto (Just fill the fields with any values)
Porting to Python 3: An In-Depth Guide (2.6 - 2.x & 3.1 - 3.x)
Practical Programming in Python - Jeffrey Elkner (PDF)
Problem Solving with Algorithms and Data Structures using Python - Bradley N. Miller and David L. Ranum
Program Arcade Games With Python And Pygame (3.3)
Programming Computer Vision with Python (PDF)
Python 2 Official Documentation (PDF, HTML, TEXT) (2.x)
Python 2.7 quick reference - New Mexico Tech (2.7)
Python 3 Official Documentation (PDF, EPUB, HTML, TEXT) (3.x)
Python Cookbook - David Beazley
Python Data Science Handbook - Jake VanderPlas (HTML, Jupyter Notebooks)
Python for Econometrics - Kevin Sheppard (PDF) (2.7.5)
Python for Everybody Exploring Data Using Python 3 - Charles Severance (PDF, EPUB, HTML)
- Python for Informatics: Exploring Information (2.7.5)
Python for you and me (2.7.3)
Python for you and me (3.x)
Python Idioms (PDF)
Python in Education (Just fill the fields with any values)
Python in Hydrology - Sat Kumar Tomer
Python Koans (2.7 or 3.x)
Python Module of the Week (3.x)
- Python Module of the Week (2.x)
Python Notes for Professionals - Compiled from StackOverflow documentation (3.x)
Python Practice Book (2.7.1)
Python Practice Projects
Python Programming (PDF) (2.6)
Scipy Lecture Notes
SICP in Python (3.2)
Snake Wrangling For Kids (3.x)
Suporting Python 3: An In-Depth Guide (2.6 - 2.x & 3.1 - 3.x)
Test-Driven Web Development with Python (3.3 - 3.x)
Text Processing in Python - David Mertz (2.3 - 2.x)
The Coder's Apprentice: Learning Programming with Python 3 - Pieter Spronck (PDF) (3.x)
The Definitive Guide to Jython, Python for the Java Platform - Josh Juneau, Jim Baker, Victor Ng, Leo Soto, Frank Wierzbicki (2.5)
The Little Book of Python Anti-Patterns (Source)
The Programming Historian - William J. Turkel, Adam Crymble and Alan MacEachern
The Python Ecosystem: An Introduction
The Python GTK+ 3 Tutorial
The Standard Python Library - Fredrik Lundh
Think Complexity - Allen B. Downey (2nd Edition) (PDF, HTML)
Web2py: Complete Reference Manual, 6th Edition (pre-release) (2.5 - 2.x)
Wikibooks: Python Programming (2.7)

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.