Shell history search command

Type history at a shell prompt:
$ history

It will display the list of all used commandline history with an serial number.

To search particular command, enter:
$ history | grep command-name
$ history | egrep -i 'scp|ssh|ftp'
Emacs Line-Edit Mode Command History Searching

To get previous command containing string, hit [CTRL]+[r] followed by search string:

(reverse-i-search):

To get previous command, hit [CTRL]+[p]. You can also use up arrow key.

CTRL-p

To get next command, hit [CTRL]+[n]. You can also use down arrow key.

CTRL-n

fc command

Apart from hostory command there are fc command to extract the command from history. The fc stands for either "find command" or "fix command.

For example list last 10 command, enter:
$ fc -l 10
To list commands 130 through 150, enter:
$ fc -l 130 150
To list all commands since the last command beginning with ssh, enter:
$ fc -l ssh
You can edit commands 1 through 5 using vi text editor, enter:
$ fc -e vi 1 5

Delete command history

The -c option causes the history list to be cleared by deleting all of the entries:
$ history -c

https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

https://www.pango.network/how-does-the-system-work/what-are-pango-lineages/

Reference paper

https://www.nature.com/articles/s41564-020-0770-5

Address of the bookmark: https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

Address of the bookmark: http://infoplatter.wordpress.com/2014/04/06/bioinformaticians-pocket-reference/

A free online version of the second edition of the book based on Stat 110, Introduction to Probability by Joe Blitzstein and Jessica Hwang, is now available at http://probabilitybook.net

Print copies are available via CRC Press, Amazon, and elsewhere. 

Stat110x is also available as an edX course. Free signup at https://www.edx.org/course/introduction-to-probability-0

The edX course focuses on animations, interactive features, readings, and problem-solving, and is complementary to the Stat 110 lecture videos on YouTube, which are available at https://goo.gl/i7njSb

The Stat110x animations are available within the course and at https://goo.gl/g7pqTo

https://projects.iq.harvard.edu/stat110/home 

Address of the bookmark: https://online.stat.psu.edu/stat414/

You can also add your favourite NGS educational material, or workshop tutorial by commenting on this bookmarks for user benefit. 

Understanding the basics of genome sequencing:

Tutorial by Luke Jostins.

http://www.genetic-inference.co.uk/blog/2009/04/basics-sequencing-dna-part-1/

http://www.genetic-inference.co.uk/blog/2009/08/basics-sequencing-dna-part-2/

A window into third-generation sequencing

http://hmg.oxfordjournals.org/content/19/R2/R227.full.pdf

==============================================

NGS data analysis pipelines

• Detecting and annotating genetic variations using the HugeSeq pipeline  DOI: 10.1038/nbt.2134
• NARWHAL, a primary analysis pipeline for NGS data http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc
• RseqFlow: Workflows for RNA-Seq data analysis  DOI: 10.1093/bioinformatics/btr441
• ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence  10.1186/1471-2164-12-285
• A framework for variation discovery and genotyping using next-generation DNA sequencing data  PubMed: 21478889
• SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects  DOI: 10.1186/1471-2105-12-134 Abstract: http://www.biomedcentral.com/1471-2105/12/134/abstract
• WEP: a high-performance analysis pipeline for whole-exome data http://www.biomedcentral.com/1471-2105/14/S7/S11
• DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. http://www.ncbi.nlm.nih.gov/pubmed/23657089
• GATK: a Toolkit for Genome Analysis http://www.broadinstitute.org/gatk/
• Metagenomics:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsmetagenomics/
• RNASeq:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsrnaseq/
• Bioinformatics and Seq courses: http://www.isb-sib.ch/training/training-activities-schedule/archive-2013.html
• Variant Detection (Model organism) Advanced tutorial https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM
• Variant Detection Introductory tutorial https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI
• Microbial de novo Assembly for Illumina Data Introductory tutorial https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM
• RNAseq Differential Gene Expression Introductory tutorial https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y

" Please add your favourite NGS link below in comment section for the benefit of bioinformatics community ". 

Address of the bookmark: http://chagall.med.cornell.edu/NGScourse/

The work will focus on the functional studies of small RNAs by using next-generation sequencing approaches.

Candidates should hold a Ph.D. degree and have strong background in bioinformatics.
The University of Nice Sophia-Antipolis provides a wide range of facilities and training essential for biomedical research.
Interested applicants should send a PDF with a cover letter stating research interests and qualifications, an updated CV, a summary of previous research experience and contact information for two references to Michele Trabucchi ( mtrabucchi@unice.fr )

Reference@http://www.datasciencecentral.com/profiles/blogs/one-page-r-a-survival-guide-to-data-science-with-r

• Templates for the Data Scientist
  1. A Template for Preparing Data: *OnePageR - *R
  2. A Template for Building Models: *OnePageR - *R
• Getting Started as a Data Scientist
  1. Getting Started with R and Rattle: *Lecture - *Laboratory
  2. Introducing and Interacting with R: *Lecture - *Laboratory
  3. BasicR - OnePage(R) - Writing R scripts
• Dealing With Data
  1. Read Data into R: *OnePageR - *R
  2. Explore and Summarise Data: *OnePageR - *R
  3. Transform Data: *OnePageR - *R
  4. Dealing with Dates and Time: (PDF, R) Dates and Time
  5. Visualising Data with GGPlot2: *OnePageR - *R
  6. Visualising Data with Maps *OnePageR - *R
  7. Spatial (R) Spatial Analysis
  8. Handling Big Data *OnePageR - *R
• Descriptive Analytics
  1. Cluster Analysis: *Lecture - *OnePageR - *R
  2. Association Analysis: *Lecture
• Predictive Analytics
  1. Decision Trees: *Lecture - *OnePageR - *R - *Rattle
  2. Ensembles of Decision Trees: *Lecture - *OnePageR - *R
  3. SVM (R)
  4. KernLab (R)
  5. NeuralNetworks (R)
  6. NNet (R)
• Model Delivery
  1. Evaluating Models: *OnePageR - *R
  2. Evaluation (R)
  3. Scoring (R)
  4. PMML (R) Exporting Models for Deployment
• Advanced Topics
  1. Text Mining: *OnePageR - *R
• Advanced R Topics
  1. Plots (PDF, R) Miscellaneous Plots
  2. Functions (PDF, R) Writing Functions in R
  3. Parallel (PDF, R) Parallel Execution
  4. Packaging (R) Pulling it Together into a Package
  5. Doing R with Style: *OnePageR - *R
  6. Literate Data Mining with KnitR: *Lecture - *OnePageR - *

More at http://www.babraham.ac.uk/our-research/nuclear-dynamics/

install and load required packages

  require(RColorBrewer)
  require(ComplexHeatmap)
  require(circlize)
  require(digest)
  require(cluster)

If all load successfully, proceed to Part 3. Otherwise, go through the following code chunks in order to ensure that each package is installed and loaded properly.

BiocManager (Morgan (2019))

Address of the bookmark: https://github.com/kevinblighe/E-MTAB-6141