<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43943?offset=10 https://bioinformaticsonline.com/bookmarks/view/26972/understanding-fastqc-output Fri, 15 Apr 2016 05:47:40 -0500 https://bioinformaticsonline.com/bookmarks/view/26972/understanding-fastqc-output <![CDATA[Understanding Fastqc Output]]> Understanding Following table and graphs

  1. Duplication level
  2. kmer profile
  3. per base GC content
  4. per base N content
  5. per base quality
  6. per base sequence content
  7. per sequence GC content
  8. per sequence quality
  9. sequence length distribution

More at http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/

Address of the bookmark: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines Sat, 21 May 2016 22:42:16 -0500 https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines <![CDATA[Bpipe - a tool for running and managing bioinformatics pipelines]]> Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

]]> Radha Agarkar https://bioinformaticsonline.com/pages/view/1161/genomics-for-bioinformatician Sat, 20 Jul 2013 07:03:00 -0500 https://bioinformaticsonline.com/pages/view/1161/genomics-for-bioinformatician <![CDATA[Genomics for Bioinformatician]]> Genomics is the study of the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping efforts. The field also includes studies of intragenomic phenomena such as heterosis, epistasis, pleiotropy and other interactions between loci and alleles within the genome. In contrast, the investigation of the roles and functions of single genes is a primary focus of molecular biology or genetics and is a common topic of modern medical and biological research. Research of single genes does not fall into the definition of genomics unless the aim of this genetic, pathway, and functional information analysis is to elucidate its effect on, place in, and response to the entire genome's networks.

Genomics was established by Fred Sanger when he first sequenced the complete genomes of a virus and a mitochondrion. His group established techniques of sequencing, genome mapping, data storage, and bioinformatic analyses in the 1970-1980s. A major branch of genomics is still concerned with sequencing the genomes of various organisms, but the knowledge of full genomes has created the possibility for the field of functional genomics, mainly concerned with patterns of gene expression during various conditions. The most important tools here are microarrays and bioinformatics. Study of the full set of proteins in a cell type or tissue, and the changes during various conditions, is called proteomics. A related concept is materiomics, which is defined as the study of the material properties of biological materials (e.g. hierarchical protein structures and materials, mineralized biological tissues, etc.) and their effect on the macroscopic function and failure in their biological context, linking processes, structure and properties at multiple scales through a materials science approach. The actual term 'genomics' is thought to have been coined by Dr. Tom Roderick, a geneticist at the Jackson Laboratory (Bar Harbor, ME) over beer at a meeting held in Maryland on the mapping of the human genome in 1986.

The outcome of almost two years of intense discussions with literally hundreds of scientists and members of the public, has three major areas of focus: Genomics to Biology, Genomics to Health, and Genomics to Society.

Genomics to Biology: 
The human genome sequence provides foundational information that now will allow development of a comprehensive catalog of all of the genome's components, determination of the function of all human genes, and deciphering of how genes and proteins work together in pathways and networks.

Genomics to Health:
Completion of the human genome sequence offers a unique opportunity to understand the role of genetic factors in health and disease, and to apply that understanding rapidly to prevention, diagnosis, and treatment. This opportunity will be realized through such genomics-based approaches as identification of genes and pathways and determining how they interact with environmental factors in health and disease, more precise prediction of disease susceptibility and drug response, early detection of illness, and development of entirely new therapeutic approaches.

Genomics to Society: 
Just as the HGP has spawned new areas of research in basic biology and in health, it has created new opportunities in exploring the ethical, legal, and social implications (ELSI) of such work. These include defining policy options regarding the use of genomic information in both medical and non-medical settings and analysis of the impact of genomics on such concepts as race, ethnicity, kinship, individual and group identity, health, disease, and "normality" for traits and behaviors.

This vision for the future of genomics is not just about the NHGRI. It encompasses the whole field of genomics, including the work of all the other Institutes and Centers at the NIH and of a number of other federal agencies. All of the NIH Institutes are already taking full advantage of the sequence and will apply its data to the better understanding of both rare and common diseases, almost all of which have a genetic component. A recent example of the way that the HGP and the knowledge and new technologies it has spawned are already facilitating science is the extremely rapid sequencing by groups in Canada and at the Centers for Disease Control and Prevention (CDC) in Atlanta of the genome of the virus that causes Severe Acute Respiratory Syndrome (SARS). The sequencing of the SARS virus genome provides insight into this new and deadly disease at a speed never before possible in science. In turn, this should lead to the rapid development of diagnostic tests and, in time, vaccines and effective treatments.

Links for the addition material available on Net

Genomes and genomics:

Bioinformatics and Genomics:

Structural genomics tutorial:

Comparative Genomics Tutorial:

GENOME TUTORIAL:

Tools and resources for identifying protein families, domains and motifs

Bioinformatics Tools 
Tips, Tutorials, and Terminology for Using Selected Resources in Genome Database Guide:

A Web-Based Comparative Genomics Tutorial for Investigating Microbial Genomes:

Free Online Tutorials Teach Anyone How to Use Genome Databases:

Circos to create concise, explanatory, unique and print-ready visualizations of your data:

Genomics and Comparative Genomics Learning Module:

Computational Challenges in Comparative Genomics

A Tutorial:

A Comparative Genomics Resource for Grains:

PLAZA: A Comparative Genomics Resource to Study Gene and Genome Evolution in Plants:

VISTA:

Software for Genomics

  1. Artemis Artemis is a free genome viewer and annotation tool that allows visualization of sequence features and the results of analyses within the context of the sequence, and its six-frame translation.
  2. Chromas It will display and prints chromatogram files from ABI automated DNA sequencers, and Staden SCF files which the analysis programs for ALF, Li-Cor and Visible Genetics OpenGene sequencers can create.
  3. Glimmer A system for finding genes in microbial DNA, especially the genomes of bacteria and archaea.Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DN
  4. Glimmer HMM A fast and accurate gene finder based on a GHMM architecture, developed specifically for eukaryotes. It incorporates splice site models adapted from the GeneSplicer program and uses interpolated Markov models for evaluating the coding regions.
  5. Glimmer M A gene finder derived from Glimmer, but developed specifically for eukaryotes. It is based on a dynamic programming algorithm that considers all combinations of possible exons for inclusion in a gene model and chooses the best of these combinations. The d
  6. MUMmer MUMmer is a system for rapidly aligning entire genomes, whether in complete or draft form.
  7. pDRAW pDRAW32 is being developed as a free time hobby project. It is far from finished, but as it has reached a point where it could be helpful for many labs, it is now available to the scientific community.
  8. Sequin Sequin is a stand-alone software tool developed by the NCBI for submitting and updating entries to the GenBank, EMBL, or DDBJ sequence databases. It is capable of handling simple submissions that contain a single short mRNA sequence, and complex submissio
  9. Staden The Staden Package consists of a series of tools for DNA sequence preparation (pregap4), assembly (gap4), editing (gap4) and DNA/protein sequence analysis (spin).

For more software @ http://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/36239/scilifelab-tutorial-for-bioinformatics-analysis Tue, 17 Apr 2018 04:33:00 -0500 https://bioinformaticsonline.com/bookmarks/view/36239/scilifelab-tutorial-for-bioinformatics-analysis <![CDATA[SciLifeLab tutorial for bioinformatics analysis !]]> SciLifeLab is a national center for molecular biosciences with focus on health and environmental research.

Courses

Old courses (2012-2014)

Metagenomics Workshop

2015 November - Uppsala
2016 November - Uppsala
2017 November - Uppsala

Introduction to Bioinformatics Using NGS Data

2015 February - Uppsala 
2015 May - Gothenburg
2015 September - Uppsala
2015 November - Lund
2016 January - Uppsala
2016 April - Linköping
2016 September - Uppsala
2016 November - Umeå
2017 January - Uppsala
2017 May - Gothenburg
2017 September - Lund
2017 November - Uppsala
2018 February - Uppsala

Introduction to Genome Annotation

2015 April - Uppsala
2016 April - Uppsala
2017 April - Uppsala
2018 May - Uppsala

De Novo Genome Assembly

2016 November - Uppsala
2017 November - Uppsala

RNA-seq course

2015 October - Uppsala
2016 April - Uppsala
2016 October - Uppsala
2017 March - Uppsala
2017 November - Uppsala
RNAseq tutorials

R Programming Foundations for Life Scientists

2016 November - Uppsala
2017 Mars - Uppsala

Single cell RNA sequencing analysis

2017 October - Uppsala

Address of the bookmark: https://scilifelab.github.io/courses/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics Tue, 31 Dec 2019 19:33:07 -0600 https://bioinformaticsonline.com/bookmarks/view/40489/machine-learning-training-and-courses-in-bioinformatics <![CDATA[Machine learning training and courses in bioinformatics !]]> Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

More tutorial at 

http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm

http://www.raetschlab.org/lectures/MLBioinformatics

http://www.raetschlab.org/lectures/bertinoro08

Book at 

https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf

Address of the bookmark: http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

]]> Rahul Nayak https://bioinformaticsonline.com/opportunity/view/43292/bioinformatics-scientist-production-bioinformatics-south-san-francisco-ca Thu, 19 Aug 2021 08:45:24 -0500 <![CDATA[Bioinformatics Scientist, Production Bioinformatics @ South San Francisco, CA]]> 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

]]> https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> 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:

]]> Jit https://bioinformaticsonline.com/news/view/39606/amity-university-bioinformatics-summer-program-kolkata Tue, 11 Jun 2019 21:27:10 -0500 https://bioinformaticsonline.com/news/view/39606/amity-university-bioinformatics-summer-program-kolkata <![CDATA[Amity University Bioinformatics Summer Program - Kolkata]]> Registrations are now open for the 2019 Summer Bioinformatics Training program at Amity University, Kolkata. The program will focus on introductory topics for life science students. We will review important history, topics and challenges bioinformatics can help address in the context of basic research, discovery and industry.

Read more: https://edu.t-bio.info/amity-university-summer-bioinformatics-program-registrations-are-open/

]]> eliabrodsky https://bioinformaticsonline.com/bookmarks/view/10394/bioinformatics-protocols Mon, 05 May 2014 10:21:41 -0500 https://bioinformaticsonline.com/bookmarks/view/10394/bioinformatics-protocols <![CDATA[Bioinformatics Protocols]]> RNA Seq

Basic Galaxy Tutorial

In this tutorial we cover the concepts of RNA-Seq differential gene expression (DGE) analysis using a very small synthetic dataset from a well studied organism.

Advanced Galaxy Tutorial

In this tutorial we compare the performance of three statistically-based differential expression tools:
* CuffDiff
* EdgeR
* DESeq2

Advanced Command Line Tutorial

You will need to install R, RStudio and cummeRbund on your PC (explained in the Tutorial). You will learn how to produce graphical output from RNA-Seq analysis previously done using a Cuffdiff analysis.

Variant Detection

Basic Galaxy Tutorial

In this tutorial we cover the concepts of detecting small variants (SNVs and indels) in human genomic DNA using a small set of reads from chromosome 22.

Advanced Galaxy Tutorial

In this tutorial we compare the performance of three statistically-based variant detection tools:
* SAMtools: Mpileup
* GATK: Unified Genotyper
* FreeBayes
Each of these tools takes as its input a BAM file of aligned reads and generates a list of likely variants in VCF format

Pipelines are for those who are comfortable with using the UNIX command line; and often allow more control over branching and iteration logic.

This is a basic variant-calling and annotation pipeline developed at the Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne. It is based around BWA, GATK and ENSEMBL and was originally designed for human (or similar) data. The master branch is configured for WGS data; there is an exome branch configured for variant calling in exome data.
To run the pipeline you will need Rubra: https://github.com/bjpop/rubra. Rubra uses the python Ruffus library: http://www.ruffus.org.uk/.

Protocols

In this protocol we discuss and outline the process of calling familial related mutations.
In this protocol we discuss and outline the process of identifying somatic variants or mutations.

Assembly

Basic Galaxy Tutorial

In this tutorial we carry out de novo assembly of a microbial genome. We have also written a De novo Genome Assembly for Illumina Data Protocol for a more generic description of the method.

Protocol

In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes. Use our Genome assembly tutorial to learn a specific case of using Galaxy to carry out de novo assembly of a microbial genome.

Small RNAs

Basic Galaxy Tutorial

This tutorial covers initial steps of the workflow for analysis of short RNA expression such as a quality control of the raw reads, processing of the raw reads for the subsequent analysis and initial quality assessment of the library.

ChIP Seq

Protocol

In this protocol we discuss ChIP-Seq: a method to analyze the interaction between proteins and DNA.

Amplicons

Protocol

In this protocol we discuss and outline the process of aligning custom amplicons using primers for high precision.

Learn Galaxy

Introduction to Galaxy, for those who are very new to Galaxy.

Using Histories and Workflows, for those with some Galaxy knowledge.

The Galaxy project website has many tutorials and screencasts about using Galaxy and the tools, and developing new tools.

Address of the bookmark: https://genome.edu.au/wiki/Learn

]]> Rahul Nayak https://bioinformaticsonline.com/pages/view/21365/a-guide-for-complete-r-beginners Fri, 20 Feb 2015 23:36:46 -0600 https://bioinformaticsonline.com/pages/view/21365/a-guide-for-complete-r-beginners <![CDATA[A guide for complete R beginners !]]> This tutorial is intended to introduce users quickly to the basics of R, focusing on a few common tasks that  biologists need to perform  some basic analysis:  load a table, plot some graphs, and perform some basic statistics. More extensive tutorials can be found on the project website and via bioconductor (not covered here).

R-language: http://www.r-project.org

BioConductor: http://www.bioconductor.org

Advantages of R

  • Free!
  • Powerful, many libraries have been created to perform application specific tasks. e.g. analysis of microarray experiments and Next-Gen sequencing (bioconductor: including Bioseq group).
  • Presentation quality graphics
    • Save as a png, pdf or svg
  • History
    • What you do can be saved for the next time you use R.
    • Ability to turn it into an automated script to perform again and again on different data

Disadvantages

Preparation

  • (Optional) Download and save the tutorial data set from
    • http://bioinformatics.knowledgeblog.org/wp-content/uploads/bioinf/kerr/data.tsv
    • Start R (type R on a Linux or Mac terminal, or find the starting link from PC)

Getting More Help

  • Project Home page
    • http://www.r-project.org/
    • Check out the ‘introduction to R’, which is a much more in depth guide .
    • Also R has a built-in help system (see later)

Working directory

This is the directory used to store your data and results. It is useful if it is also the directory where your input data is stored.

  • Mac/Linux: this is the directory where you typed in R
  • PC: Change using the change working directory option
]]> Archana Malhotra