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 - *

Applications in plain paper are invited for the posts of (1) Traineeship (2 Nos.) and (2) Studentship – (2 Nos.) in the DBT funded-Bioinformatics Infrastructure Facility (BIF), Nagaland University, Lumami-798627, Nagaland. Details are given below. Interested candidates may submit the application along with self attested copies of certificates in support of the candidature to Prof. Chitta Ranjan Deb, Coordinator or Dr. L. N. Kakati, Deputy Coordinator, BIF Centre, Nagaland University, Lumami-798627, Nagaland on or before 15th January 2015.

The scanned application with relevant documents may be sent by email attachment to bifnulumami@gmail.com. Shortlisted candidates will be informed by email if called for interview. No TA/DA is admissible for attending the interview.

Traineeship (Two nos.)

Post Graduate degree in any branch of Life Sciences from UGC recognized Universities

Knowledge of computers and bioinformatics

Rs.8000/- p.m. fixed.

6 months

Studentship (Two nos.)

Pursuing Post Graduate degree in any branch of Life Sciences from UGC recognized Universities

Knowledge of computers and bioinformatics

Rs.8000/- p.m. fixed.

6 months

Terms and Conditions:

i) Applicants need to produce all original documents if call for interview.
ii) The posts are purely temporary and the appointment does not confer any entitlement or right over the job and will not be considered as formal service.
iii) No TA & DA will be paid for appearing in the walk-in-interview.
iv) The stipend/salary amount is subject to the sanction of DBT, New Delhi.

Advertisement: http://www.nagauniv.org.in/files/BIF%20Advt.pdf

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.

Qualifications Essential:
Doctoral degree in Bio-informatics/ Biotechnology/Molecular Biology and Biotechnology/Life Sciences/ Computer Sciences with specialization in Bio-informatics including relevant basic sciences with 8 years’ experience in the relevant subject as Scientist/Lecturer or in an equivalent position in the Pay Band- 3 of 15600-39100 with Grade Pay of 5400/6000/7000/8000 having made contribution to research/teachin/extension education as evidenced by published work/innovations and impact.

OR

Doctoral degree in the above subject(s) including relevant basic sciences with minimum 8 years’ experience of high quality post-doctoral research in an institution/organization as evidenced by at least 6 publications in journals with NAAS rating of 7.5 or above.

Desirable:
(i) Specialization and experience: Knowledge of software development and its application in crop bioinformatics, experience in handling ‘omies’data.
(ii) Teaching experience in relevant subject

The Secretary, Agricultural Scientists Recruitment Board, Indian Council of Agricultural Research Krishi Anusandhan Bhavan-I, Pusa New Delhi –110 012, India

Detailed eligibility criteria with how to apply information can be had at:
http://www.indiastudychannel.com/jobs/333467-Indian-Council-of-Agricultural-Research-looking-for-Assistant-Director-General.aspx

More at http://asrb.org.in/administrator/uploads_dir/1418978057english.pdf

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

The developers and expert trainers are the authors of several popular R packages, including ggplot2, plyr, lubridate, and others.

More at http://www.rstudio.com/

http://www.rstudio.com/products/RStudio/

Address of the bookmark: http://www.rstudio.com/

Address of the bookmark: https://training.galaxyproject.org/training-material/topics/assembly/tutorials/unicycler-assembly/tutorial.html

Website: www.actrec.gov.in; Ph: 27405000

No. ACTREC/Advt./ 66 /2014 23rd December, 2014
Research Associate

International Cancer Genome Consortium (ICGC) - India Project (IRB Project No. 3 A/c. No. 2408)

Dr. Rajiv Sarin

Duration of the Project: One year Extendable up to Three years.

Consolidated Salary: Rs. 42,000/- p.m.

Application last date: 8th January, 2015.

Interview Date & Time: 21st January, 2015, at 11.00 a.m.

Venue: Conference Room, 3rd floor, Khanolkar Shodhika, ACTREC.

Essential Qualifications and Experience:

Ph.D (any branch of Life Sciences)

The candidate must have at least one year experience after Ph.D., preferably in Genomics and Molecular Biology.

Candidates fulfilling these requirements should pre register themselves by sending their application in the prescribed format with recent CV and contact details of 2 referees by e-mail to icgc@actrec.gov.in latest 8th January, 2015 by 10.00 a.m.

Candidates shortlisted for the interview will be intimated by email on or before 9th January, 2015.

The interviews would be held on 21st January 2015 and will be only for the pre registered candidates who have been shortlisted.
No T.A./D.A. will be admissible for attending the interview.

At the time of Interview the candidate should bring original certificates along with CV with contact details of 2 referees and submit the photocopies (attested) of the certificates, with a recent passport size photograph.

Advertisement: www.actrec.gov.in/data%20files/2014/Walk-in-Research-Fellow-26-12-14.doc

For example ... contig-stats.pl is a Perl script that will automatically describe features of a sequence assembly.

http://milkweedgenome.org/?q=scripts

Address of the bookmark: http://milkweedgenome.org/?q=scripts