“It’s like a subway map,” she said, “but for DNA.”

That idea is exactly what Sequence Tube Map brings to life.

Instead of showing a genome as one long, confusing sequence, it displays different genomic paths like routes on a metro map. Where sequences are shared, the paths travel together. Where genetic variation occurs, they branch into different routes and may join again later.

This makes complex genome graphs much easier to explore.

Maya could now see how different haplotypes moved through the same genomic region and where they took different paths. She could even view sequencing reads mapped onto the graph, helping her understand what was happening in regions with genetic variation.

What once looked like a tangled network now looked like a journey.

Sequence Tube Map turns the complexity of graph genomes into a visual story—making it easier for scientists to see how DNA can take different routes.

Read more at  https://github.com/vgteam/sequencetubemap 

and

https://academic.oup.com/bioinformatics/article/35/24/5318/5542397

Demo at https://vgteam.github.io/sequenceTubeMap/

Our main research themes are:

Domain annotation and metagenomics
Transcriptomics and sequence analysis
Protein evolution and interactions
Protein conformational dynamics

More at http://www.lcqb.upmc.fr/AnalGenom/home.html

Instead of asking only whether two genes occur together across genomes, CORGIAS asks a deeper question: did these genes actually evolve together, repeatedly gaining or losing their presence in concert, or are they merely travelling together because of their inherited history? The framework introduces two complementary approaches, Ancestral State Adjustment (ASA) and the Simultaneous EVolution test (SEV), which incorporate evolutionary information into the search for gene associations. This distinction becomes increasingly important as genome sequencing continues to uncover enormous numbers of microbial genomes, many from organisms that have never been cultured and whose genes remain functionally mysterious. In this growing genomic landscape, simply finding a gene is no longer enough—we need clues about what that gene might be doing. CORGIAS approaches this problem almost like a genomic detective: rather than treating every association as evidence, it reconstructs the evolutionary story behind the association and asks whether the evidence survives that history. By doing so, it offers a way to separate coincidence from biological connection and potentially uncover functional relationships hidden within the vast microbial genomic landscape. The broader message is beautifully simple: genes do not evolve in isolation, and understanding what they do may require us to look beyond where they are today and reconstruct how they arrived there. Sometimes, the most important clue in a genome is not the gene itself, but the evolutionary journey it has taken.

More at https://academic.oup.com/nargab/article/7/4/lqaf182/8377910?

Bioinformatics
Cancer
Epigenetics

More at http://liulab.dfci.harvard.edu/

Installation

General Usage Guide

Data Preprocessing Guide

Specific Tool Guides:

• BBDuk
• BBMap
• BBMask
• BBMerge
• BBNorm
• CalcUniqueness
• Clumpify
• Dedupe
• Reformat
• Repair
• Seal
• Split Nextera
• Statistics
• Tadpole
• Taxonomy

https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/

Address of the bookmark: https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/

Near Basant Vihar G.T. Road Bypass P.O. Box No.129

Karnal - 132001 (Haryana)

WALK-IN-INTERVIEW

A walk-in-Interview is proposed to be held at National Bureau of Animal Genetic Resources, Karnal (Haryana)-132001 at 10:30 AM on 10.06.2015 to select One Research Associate as per details given below:

1. One post of Research Associate under National Fellow project entitled “Genome data mining to unravel molecular basis of thermotolerance and adaptation to diverse environments in native cattle and buffaloes”.

The post duration is Upto 22.05.2016 or earlier & Co-terminus with the project.

Essential Qualifications: Master’s degree (M.Sc. / M.V.Sc.) in Biotechnology/ Animal Genetics and Breeding/ Life Sciences/ Bioinformatics with 2 Years research experience in relevant subject or Ph.D in any of the above subjects.

Desirable: Working Experience in molecular biology, gene expression/ microarray data analysis, SNP genotyping and sequence data analysis, mammalian cell-culture handling etc.

Emolument: Rs. 23,000/- per month + HRA as per admissibility

Research Associate: ONE

Duration of engagement: Upto 22.05.2016 or earlier Co-terminus with the project

Age Limit:  40 years for Men  45 years for women as on date of interview

Note: Relaxation in age will be admissible for SC/ST & OBC candidates as per Govt. of India /ICAR norms

1. The applicants must bring with them original documents and brief of research work done during post graduation along with a set of photocopy and latest two passport size photographs. 2. A panel of selected candidates will also be made which may be utilized for filling of positions of shorter durations in future if demand arises. 3. Experience certificate in original, if any 4. The above positions are purely on temporary basis and are coterminus with the project. No TA/DA will be paid to attend the interview. 5. Any other clarifications can be had on the date of interview. 6. The Director’s decision will be final and binding on all respects.

Advertisement: http://210.212.93.85/RAadvertisiment.pdf

http://funprogramming.org/

Post: Junior Research Fellow (1 Position)

Duration: Three years

Qualification: First class in M.Sc/M.tech in Bioinformatics/Life Sciences/Biophysics/ Biostatistics/Bioengineering. Experience in Database development, NGS data analysis, Systems Biology and Structural Bioinformatics is desired. Preference will be given to the candidates with good computer programming skills in C, C++, R, Perl, PHP, Unix Scripting etc.

Selected candidates will be paid fellowship as per existing DST norms.

How to apply:

Candidates are requested to apply through one of the two modes given below
1. Online application – Click here to submit the online application https://docs.google.com/forms/d/16h2GLnQ-Ny-tLtlgfY3Bx3sCjeHJE30cfhJaDqW_uRs/viewform?c=0&w=1
2. Application forms can be downloaded from here.https://docs.google.com/file/d/0BwwJEudQStxFWXdNWXl4NWtDaWc/edit
Filled in application form should be sent by post to Dr. D. Bharanidharan, Department of Bioinformatics, Aravind Medical Research Foundation No 1, Anna Nagar Madurai – 625 020,

Candidates should apply by online or submit their applications by post on or before 15th June, 2015. Only Short listed candidates will be called for an interview. No TA/DA will be paid.

• -e Makes the line of code be executed instead of a script
• -n Forces your line to be called in a loop. Allows you to take lines from the diamond operator (or stdin)
• -p Forces your line to be called in a loop. Prints $_ at the end

• This counts the number of quotation marks in each line and prints it

  perl -ne '$cnt = tr/"//;print "$cnt\n"' inputFileName.txt

• Adds string to each line, followed by tab

  perl -pe 's/(.*)/string\t$1/' inFile > outFile

• Append a new line to each line

  perl -pe 's//\n/' all.sent.classOnly > all.sent.classOnly.sep

• Replace all occurrences of pattern1 (e.g. [0-9]) with pattern2

  perl -p -i.bak -w -e 's/pattern1/pattern2/g' inputFile

• Go through file and only print words that do not have any uppercase letters.

  perl -ne 'print unless m/[A-Z]/' allWords.txt > allWordsOnlyLowercase.txt

• Go through file, split line at each space and print words one per line.

  perl -ne 'print join("\n", split(/ /,$_));print("\n")' someText.txt > wordsPerLine.txt

• or in other words, delete every character that is not a letter, white space or line end (replace with nothing)

  perl -pne 's/[^a-zA-Z\s]*//g' text_withSpecial.txt > text_lettersOnly.txt

• perl -pne 'tr/[A-Z]/[a-z]/' textWithUpperCase.txt > textwithoutuppercase.txt;

• Print only the second column of the data when using tabular as a separator

  perl -ne '@F = split("\t", $_); print "$F[1]";' columnFileWithTabs.txt > justSecondColumn.txt

• One-Liner: Sort lines by their length

  perl -e 'print sort {length $a <=> length $b} <>' textFile

• One-Liner: Print second column, unless it contains a number

  perl">perl -lane 'print $F[1] unless $F[1] =~ m/[0-9]/' wordCounts.txt

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/