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/

ICRISAT seeks applications from Indian nationals Visiting Scientist-Computational Genomics (2 positions), to be part of a team of Centre of Excellence in Genomics (CEG), (www.icrisat.org/ceg) to work on legume genomics projects. The positions will be based at ICRISAT’s Headquarters in Patancheru, Hyderabad, India.

ICRISAT is a non-profit, non-political organization that conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. Covering 6.5 million square kilometers of land in 55 countries, the semi-arid tropics is home to over 2 billion people, with 650 million of these are the poorest of the poor. ICRISAT and its partners help empower those living in the semi-arid tropics, especially smallholder farmers, to overcome poverty, hunger, malnutrition and a degraded environment through more efficient and profitable agriculture. ICRISAT is headquartered in Greater Hyderabad, Andhra Pradesh, India and belongs to the Consortium of Centers supported by the Consultative Group on International Agricultural Research (CGIAR).

The Job: Responsibilities for these positions include:

Analyzing and handling large-scale next generation sequencing DNA and RNA data
Data mining and development of pipelines and troubleshooting
Genome diversity analysis such as SNPs, Indels, Structural Variations, population structure
Genome wide association study (GWAS) related analysis- LD analysis, hapmap and trait mapping
Expression analysis based on RNA-Seq data, annotation, gene ontology and metabolic pathway analysis
Epigenome analysis, small RNA identification
Gene family analysis, sequence level protein analysis, orthology/paralogy and molecular modelling
Compiling and analysis of results, writing reports and research papers

The Person: Ph.D. or MSc/MTech/PGDCA with two years research experience in Biotechnology, Computational biology, Agricultural/ Plant Biotechnology, Genetics, Molecular Biology or related discipline. Good knowledge of programming/scripting in at least two of following languages: Perl, C, C++, R, Shell Scripting and Python is plus.

How to apply: Please apply latest by 20 July 2014. The application should include the name of the position applied for, a letter of motivation, a full Curriculum Vita (CV), and the names and contact information of three references that are knowledgeable of the candidate’s professional qualifications and work experience. Technical details and more information about these positions can be obtained from R.K.VARSHNEY@CGIAR.ORG. All applications will be acknowledged, however only short listed candidates will be contacted.

Apply here https://recruit.zoho.com/ats/Portal.na?digest=T642sgLYWZOStExJ77cPrcM*sIMGZETWw4yPxngbmHA-

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/

Address of the bookmark: http://www.programmingr.com/category/stype/r-job-listings/

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 - *

Perl one-liners are small and awesome Perl programs that fit in a single line of code and they do one thing really well. These things include changing line spacing, numbering lines, doing calculations, converting and substituting text, deleting and printing certain lines, parsing logs, editing files in-place, doing statistics, carrying out system administration tasks, updating a bunch of files at once, and many more. Perl one-liners will make you the shell warrior. Anything that took you minutes to solve, will now take you seconds!

perl -pe '$\="\n"'   
#double space a file

perl -pe '$_ .= "\n" unless /^$/'
#double space a file except blank lines

perl -pe '$_.="\n"x7'
#7 space in a line.

perl -ne 'print unless /^$/'
#remove all blank lines

perl -lne 'print if length($_) < 20'
#print all lines with length less than 20.

perl -00 -pe ''
#If there are multiple spaces, delete all leaving one(make the file a single spaced file).

perl -00 -pe '$_.="\n"x4'
#Expand single blank lines into 4 consecutive blank lines

perl -pe '$_ = "$. $_"'
#Number all lines in a file

perl -pe '$_ = ++$a." $_" if /./'
#Number only non-empty lines in a file

perl -ne 'print ++$a." $_" if /./'
#Number and print only non-empty lines in a file

perl -pe '$_ = ++$a." $_" if /regex/'
#Number only lines that match a pattern

perl -ne 'print ++$a." $_" if /regex/'
#Number and print only lines that match a pattern

perl -ne 'printf "%-5d %s", $., $_ if /regex/'
#Left align lines with 5 white spaces if matches a pattern (perl -ne 'printf "%-5d %s", $., $_' : for all the lines)

perl -le 'print scalar(grep{/./}<>)'
#prints the total number of non-empty lines in a file

perl -lne '$a++ if /regex/; END {print $a+0}'
#print the total number of lines that matches the pattern

perl -alne 'print scalar @F'
#print the total number fields(words) in each line.

perl -alne '$t += @F; END { print $t}'
#Find total number of words in the file

perl -alne 'map { /regex/ && $t++ } @F; END { print $t }'
#find total number of fields that match the pattern

perl -lne '/regex/ && $t++; END { print $t }'
#Find total number of lines that match a pattern

perl -le '$n = 20; $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $m'
#will calculate the GCD of two numbers.

perl -le '$a = $n = 20; $b = $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $a*$b/$m'
#will calculate lcd of 20 and 35.

perl -le '$n=10; $min=5; $max=15; $, = " "; print map { int(rand($max-$min))+$min } 1..$n'
#Generates 10 random numbers between 5 and 15.

perl -le 'print map { ("a".."z",”0”..”9”)[rand 36] } 1..8'
#Generates a 8 character password from a to z and number 0 – 9.

perl -le 'print map { ("a",”t”,”g”,”c”)[rand 4] } 1..20'
#Generates a 20 nucleotide long random residue.

perl -le 'print "a"x50'
#generate a string of ‘x’ 50 character long

perl -le 'print join ", ", map { ord } split //, "hello world"'
#Will print the ascii value of the string hello world.

perl -le '@ascii = (99, 111, 100, 105, 110, 103); print pack("C*", @ascii)'
#converts ascii values into character strings.

perl -le '@odd = grep {$_ % 2 == 1} 1..100; print "@odd"'
#Generates an array of odd numbers.

perl -le '@even = grep {$_ % 2 == 0} 1..100; print "@even"'
#Generate an array of even numbers

perl -lpe 'y/A-Za-z/N-ZA-Mn-za-m/' file
#Convert the entire file into 13 characters offset(ROT13)

perl -nle 'print uc'
#Convert all text to uppercase:

perl -nle 'print lc'
#Convert text to lowercase:

perl -nle 'print ucfirst lc'
#Convert only first letter of first word to uppercas

perl -ple 'y/A-Za-z/a-zA-Z/'
#Convert upper case to lower case and vice versa

perl -ple 's/(\w+)/\u$1/g'
#Camel Casing

perl -pe 's|\n|\r\n|'
#Convert unix new lines into DOS new lines:

perl -pe 's|\r\n|\n|'
#Convert DOS newlines into unix new line

perl -pe 's|\n|\r|'
#Convert unix newlines into MAC newlines:

perl -pe '/regexp/ && s/foo/bar/'
#Substitute a foo with a bar in a line with a regexp.

Reference/Sources:

http://genomics-array.blogspot.in/2010/11/some-unixperl-oneliners-for.html

http://genomespot.blogspot.com/2013/08/a-selection-of-useful-bash-one-liners.html

http://biowize.wordpress.com/2012/06/15/command-line-magic-for-your-gene-annotations/

http://genomics-array.blogspot.com/2010/11/some-unixperl-oneliners-for.html

http://bioexpressblog.wordpress.com/2013/04/05/split-multi-fasta-sequence-file/

What are SNPs?
SNPs (pronounced "snips") are positions in the genome where individuals differ by a single nucleotide. For example:

Reference: ...A T G C A T G A...
Variant:     ...A T G T A T G A...

Here, the C in the reference genome has been replaced by a T in the variant.

SNPs occur roughly every 300–1,000 bases in the human genome, meaning there are millions of them scattered throughout our DNA. Most SNPs have no effect on health, but some are linked to disease susceptibility, drug response, and other traits.

Why Do We Analyze SNPs?
1. Medical Genetics

Identify disease-associated variants (e.g., BRCA1/2 in breast cancer).

Predict drug response (pharmacogenomics).

Enable precision medicine by tailoring treatments.

2. Population Genetics & Ancestry

Trace human migration and ancestry.

Study genetic diversity within and between populations.

3. Agriculture & Animal Breeding

Select for desirable traits (drought resistance, yield, disease resistance).

Improve breeding efficiency in livestock.

4. Evolutionary Biology

Track natural selection.

Study adaptation in wild populations.

How is SNP Analysis Performed?
SNP analysis can be broadly divided into three steps:

SNP Detection
Genotyping arrays: Chips that test hundreds of thousands of known SNP positions simultaneously. Fast and affordable, widely used in consumer ancestry testing.

Whole-genome or whole-exome sequencing: Can detect known and novel SNPs across the genome.

Targeted sequencing or PCR: For focused analysis of specific regions.

Variant Calling
Sequencing data is aligned to a reference genome. Bioinformatics tools (e.g., GATK, bcftools) identify positions where the sequenced sample differs from the reference.

Annotation and Interpretation
Tools (e.g., SnpEff, VEP) predict the functional impact of SNPs.

Are the SNPs in coding regions? Do they cause amino acid changes? Are they known to be pathogenic?

Databases like dbSNP, ClinVar, and GWAS Catalog provide information on known associations.

Common Tools for SNP Analysis
Alignment: BWA, Bowtie2

Variant Calling: GATK, FreeBayes

Visualization: IGV, UCSC Genome Browser

Annotation: SnpEff, VEP

Statistical Analysis: PLINK, SNPTEST

Challenges in SNP Analysis
False positives/negatives: Sequencing errors, alignment issues.

Population stratification: Confounding in association studies.

Interpretation: Many SNPs have unknown or complex effects.

Researchers address these with rigorous quality control, large datasets, and increasingly sophisticated statistical models.

The Future of SNP Analysis
With advances in sequencing technology and AI-driven analysis, SNP studies are expanding:

Polygenic risk scores predict disease risk based on thousands of SNPs.

Large-scale biobanks (e.g., UK Biobank, All of Us) enable powerful genome-wide association studies (GWAS).

CRISPR and functional assays help validate SNP effects in the lab.

SNP analysis is at the heart of the genomic revolution, promising insights into biology, health, and evolution at unprecedented scale.

Conclusion
From diagnosing rare diseases to designing better crops, SNP analysis is a foundational tool in modern science. As our ability to sequence and interpret genomes improves, so will our understanding of these tiny—but mighty—variations in DNA.

Following are the useful links:

Single Cell RNAseq data analysis Tutorial

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

SCell: single-cell RNA-seq analysis software

https://github.com/diazlab/SCell

Beta-Poisson model for single-cell RNA-seq data analyses

https://github.com/nghiavtr/BPSC

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

https://research.cchmc.org/pbge/sincera.html

SC3 – consensus clustering of single-cell RNA-Seq data

http://biorxiv.org/content/early/2016/09/02/036558

Citrus: A toolkit for single cell sequencing analysis

http://biorxiv.org/content/early/2016/09/14/045070

Single-Cell Resolution of Temporal Gene Expression during Heart Development

http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

http://biorxiv.org/content/early/2016/11/15/087775

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

http://biorxiv.org/content/early/2016/11/21/088856

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

https://github.com/hmatsu1226/SCOUP

scLVM is a modelling framework for single-cell RNA-seq data

https://github.com/PMBio/scLVM

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

https://github.com/jw156605/SLICER

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

http://www.morgridge.net/SinQC.html

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

https://github.com/zji90/TSCAN

Visualization and cellular hierarchy inference of single-cell data using SPADE

http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

OEFinder: Identify ordering effect genes in single cell RNA-seq data

https://github.com/lengning/OEFinder

7th ADVERTISEMENT – 2014 (2)

INSPIRE Faculty Scheme: a component of “Assured Opportunity for Research Career (AORC)” under INSPIRE.

The Department of Science and Technology, Government of India, has launched the “Innovation in Science Pursuit for Inspired Research (INSPIRE)” [http://www.inspire-dst.gov.in] program in 2008.

The program aims to attract talent for study of science and careers with research. INSPIRE includes many components. The importance of Assured Career Opportunity in R&D sector has been recognized.

INSPIRE Faculty Scheme opens up an “Assured Opportunity for Research Career (AORC)” for young researchers in the age group of 27-32 years. It offers a contractual research awards to young achievers and opportunity for independent research in the near term and emerge as a future leader in the long term.

Eligibility

Essential Indian citizens and people of Indian origin including NRI/PIO status with PhD (in science, mathematics, engineering, pharmacy, medicine, and agriculture related subjects) from any recognized university in the world,

Those who have submitted their PhD Theses and are awaiting award of the degree are also
eligible. However, the award will be conveyed only after confirmation of the awarding the
PhD degree.

The upper age limit as on 1st July 2014 should be 32 years for considering support for a
period of 5 years. However, for SC and ST candidates, upper age limit will be 35 years.

Publication(s) in highly reputed Journals demonstrating research potential of the candidate.

Desirable

Candidates who are within top 1% at the School Leaving Examination, IIT-JEE rank, 1st Rank Holder either in graduation or post-graduation level university examination (which are used presently for identifying INSPIRE Scholars at under-graduate level and INSPIRE Fellows for doctoral degree)

More at http://www.inspire-dst.gov.in/faculty_scheme.html