If you have any questions or comments, post them on one of the trackers on BRIG’s SourceForge page: http://sourceforge.net/tracker/?group_id=328245.

Features:

• Images show similarity between a central reference sequence and other sequences as concentric rings.
• BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.
• Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.
• Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.
• BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously

Address of the bookmark: http://brig.sourceforge.net/

Address of the bookmark: http://nemo2.sourceforge.net/index.html

There are common options for most BBMap suite programs and depending on the file extension the input/output format is automatically chosen/set.

Using BBMap

Mapping Nanopore reads

BBMap.sh has a length cap of 6kbp. Reads longer than this will be broken into 6kbp pieces and mapped independently.

More at https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html

Address of the bookmark: https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html

Beagle version 4.1 has a more accurate genotype phasing algorithm and a very fast and accurate genotype imputation algorithm. Version 4.1 also has several changes to the command line arguments which are described in the release notes. The "ped" argument has no effect in version 4.1. If your data contains nuclear families and you want to model the parent-offspring relationships when phasing genotypes, please use version 4.0.

If you use Beagle 4.1 in a published analysis, please report the program version and cite the appropriate article.

The citation for Beagle's phasing algorithm is:

S R Browning and B L Browning (2007) Rapid and accurate haplotype phasing and missing data inference for whole genome association studies by use of localized haplotype clustering. Am J Hum Genet 81:1084-1097.doi:10.1086/521987

The citation for Beagle's genotype imputation algorithm is:

B L Browning and S R Browning (2016). Genotype imputation with millions of reference samples. Am J Hum Genet 98:116-126.doi:10.1016/j.ajhg.2015.11.020

The citation for Beagle's IBD detection algorithm is:

B L Browning and S R Browning (2013). Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194(2):459-71.doi:10.1534/genetics.113.150029

Address of the bookmark: http://faculty.washington.edu/browning/beagle/beagle.html

This is necessary in light of the increasing use of higher level statistics in biomedical research. I hope it accomplishes this mission and encourage its free distribution and use as a course text or supplement.

K Seefeld, May 2007

ESSENTIAL QUALIFICATIONS:
We are looking for a motivated candidate with keen interest in bioinformatics and microbial genome evolution. The candidate must have a Master’s Degree in Bioinformatics, Computational Biology, Computer Science, Microbiology, Biology or a related field with good academic record.

DESIRABLE QUALIFICATIONS
Hands on research experience on handling next generation sequencing data and phylogenetic reconstruction methods. Excellent programming skills (Perl/Python/Java/Php) and experience in working on Unix/Linux platform is preferred. Furthermore; good knowledge is required in statistics (R/Matlab) and the application of bioinformatics analysis tools.

AGE:
Below 28 years as on 15th November, 2016.

EMOLUMENTS:
Rs. 25,000 + 20% HRA for NET/GATE qualified and Post Graduate in Professional Degree course qualified candidates and
Rs. 12,000/- + 20% HRA for others.

DURATION:
Initial appointment will be given for one year and further extension will be based on the performance till termination of the project.
Only those fulfilling the above criteria need apply and will be called for interview. In the event of more than 10 candidates being short-listed by screening the applications, a written test will be conducted before the selection interview and only those who are successful in the written test will be interviewed. No TA/ DA will be given for appearing in the interview.

Suitably qualified candidates may send applications in the prescribed format (Download here) with a photograph, a copy of full resume indicating the percentage of Marks obtained and attested photocopies of credentials & experience to reach the undersigned on or before 15th November, 2016. Envelopes must be superscripted with abbreviated title of the project, advertisement number and job title. Selection to the position will not entitle the candidate to any future positions at RGCB (permanent or otherwise). As with all project positions at RGCB, the position will be co terminus with end of the project.

Date: Nov 11th. 2016 Last Date: Nov 25th. 2016

PROJECT ID: 632

The following posts are urgently required to be filled for the Department of Biotechnology, Government of India funded project entitled "Computational Core for Plant Metabolomics" administrated by Prof Indira Ghosh, School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110 067

NB:For all Bioinformatics posts, preference will be given to candidates with a good knowledge of Python and/or R. Knowledge of JAVA will also get a special consideration.

RA / Research Associate (Metabolic engineering/Computational Biologist)

Salary: Rs. 36000/- + HRA
Vacancy: 1
Essential Qualifications: PhD in Bioinformatics /Mathematics/Computer Science with experience in analyzing high throughput omics-based data/ system Biology/ Analysis of Network Biology. Published paper in the field is a must to prove the experience.
Desired Skills: Prior experience in handling and guiding bioinformatics, metabolomics data, planning of new research area in metabolic driven network , managing the project portal, preparing and filing reports etc. Will be expected to communicate with user groups and coordinate with LIMS group in Hyderabad and the Cheminformatics group in Delhi.

RA / Research Associate (Chemo-informatics/Computational Biologist)

Salary: Rs. 36000/- + HRA
Vacancy: 1
Essential Qualifications: PhD in Bioinformatics/ computational biology/ Biophysics/Computer Science. Computational and Chemical structure related experience is a necessary qualification proven by paper published and program developed.
Desired Skills: Research experience in Chemical scaffold mapping, in silico Spectral analysis, Biological Database Designing & Integration is required. Individual is responsible to develop methods related to metabolite identification, Testing and refining and integrate LIMS with IIIT Hyderabad and will be expected to communicate with user groups.

Project SRF (Bioinformatics/Programming)

Salary: As per DBT rules
Vacancy: 1
Essential Qualifications: Masters/B Tech in Basic Sciences with at least 2yrs of research experience in Bioinformatics/Computational Biology related to Database /portal building & maintenance ,high throughput data handling and analysis etc. For M.Sc/B.Tec, Published paper in peer-reviewed Journal and for M.Tech, thesis submission in computational biology is a must.

More at http://www.jnu.ac.in/Career/currentjobs.htm

https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA

Address of the bookmark: https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA

Address of the bookmark: https://readthedocs.org/projects/bipype/

Structural variants (SVs) such as deletions, insertions, duplications, inversions and translocations litter genomes and are often associated with gene expression changes and severe phenotypes (ie. genetic diseases in humans). Recent studies on the functional aspects of different types of SVs have unveiled several cases of adaptive evolution. For example, inversions have been associated with ecological adaptations and may facilitate speciation. Due to their prevalent nature, SVs arguably have a large impact on genome evolution and should not be neglected when studying the genetics of adaptation and speciation. SVs were classically defined as chromosomal rearrangements larger than 1kb, but due to a higher resolution of new detection methods, smaller variants (between 50 and 1000 base pairs) can now be accurately assessed. Besides various methods of detection in next generation sequencing data (paired end mapping, split reads, and depth of coverage), array-based approaches have proven to be particularly useful for detecting copy number variations (CNVs). These technologies have enabled researchers to catalog a wide spectrum of SVs in many organisms and infer the effects of selection shaping their evolutionary trajectories.

Structure variation sequencing signature (Source: NatRev Genetics)

Related tools, databases and publications are listed below. If you know any interesing papers, please let us know in comment section:

Key concepts

Structural variation includes balanced variants such as inversions and translocations, and unbalanced ones such as duplications and deletions (copy number variations or CNVs).

Structural variants can arise by several mechanisms, including nonallelic homologous recombination (NAHR), nonhomologous end‐joining (NHEJ) and DNA replication‐based fork stalling and template switching (FoSTeS).

CNV is closely linked to segmental duplication, but is not exactly the same. Segmental duplications can stimulate CNV formation by NAHR, and themselves arise from CNVs that have become fixed.

Segmental duplications did not appear uniformly during the evolution of the Great Ape species, but rather during a burst of activity around the time of the divergence of gorilla from the human/chimpanzee ancestor.

Duplicated genes play a critical role in the evolution of a genome as they act as ‘spare parts’ than can evolve to perform new or more specialized functions.

Effects of structural variation on gene expression can be identified but only a few examples of the consequences for species biology have been documented.

Tools

CNVnatora tool for CNV discovery and genotyping from depth of read mapping.2011a,2011b

AGEa tools that implements an algorithm for optimal alignment of sequences with SVs.2011

BreakSeqa pipeline for annotation, classification and analysis of SVs at single nucleotide resolution.2010

PEMera computational and simulation framework for discovering SVs by paired-end read mapping.2009,2007

GASV https://code.google.com/archive/p/gasv/

PAIROSCOPE http://pairoscope.sourceforge.net/

SVDetect http://svdetect.sourceforge.net/Site/Home.html

BreakPtr, discovery of unbalanced structural variants (copy-number variants) with tiling microarrays Link 

R Package https://www.bioconductor.org/help/course-materials/2010/EMBL2010/Practical-4-StructuralVariants.pdf

BreakSeq, structural variant genotyping using split reads Link 

CopySeq, genotyping of unbalanced structural variants (copy-number variants) using read-depth Link 

DELLY2, integrated structural variant discovery, genotyping and visualization in deep sequencing data Link 

PEMer, structural variant discovery in 454 sequencing data by paired-end mapping Link 

TIGER, transduction inference in germline genomes using short read data Link 

MANTA https://github.com/Illumina/manta

SV-Bay https://github.com/InstitutCurie/SV-Bay

BreakDancer http://breakdancer.sourceforge.net/

Variation Hunter http://compbio.cs.sfu.ca/software-variation-hunter

Lumpy https://github.com/arq5x/lumpy-sv

ForestSV http://sebatlab.ucsd.edu/index.php/software-data 

PBSuites for long reads https://sourceforge.net/projects/pb-jelly/

Visualization

The SV visualization tool: http://genomesavant.com/savant/

InGAP-SV (http://ingap.sourceforge.net/) that is nice tools for both detection and visualisation of severals kind of structural variations (Large insertions, translocation, deletion, inversions....) 

Tools table: http://www.nature.com/nbt/journal/v29/n8/fig_tab/nbt.1904_T2.html

Variation Viewer https://www.ncbi.nlm.nih.gov/variation/view/

Papers

http://www.nature.com/nmeth/journal/v9/n2/full/nmeth.1858.html

http://journal.frontiersin.org/researchtopic/1412/structural-variations-in-genomes-ecological-and-evolutionary-implications

http://www.mi.fu-berlin.de/wiki/pub/ABI/GenomicsLecture10Materials/structural-variation.pdf

http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-1479-3

https://www.ncbi.nlm.nih.gov/dbvar/content/overview/

http://www.nature.com/subjects/structural-variation

https://eichlerlab.gs.washington.edu/news/NatMeth_Feb2012.pdf

https://www.ncbi.nlm.nih.gov/pubmed/19477992 ***

https://www.ncbi.nlm.nih.gov/pubmed/22452995

http://biorxiv.org/content/early/2016/09/06/073833

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479793/

http://www.nature.com/articles/srep18501

http://www.genetics.org/content/202/1/351

http://www.cs.cmu.edu/~sssykim/teaching/s13/slides/Lecture_SVI.pdf

https://www.omicsonline.org/open-access/structural-variation-detection-from-next-generation-sequencing-2469-9853-S1-007.php?aid=69055

http://schatzlab.cshl.edu/presentations/2016/2016.01.12.PAG.Structural%20Variations.pdf