I have used the following assemblers

• Spades (v. 3.10.1)
• CANU (v. 1.6)
• Unicycler (v. v0.4.1)
• Miniasm (v. 0.2-r137-dirty)

I have used the following mappers

• minimap2 (v. 2.0rc1-r232)
• minimap (v. 0.2-r124-dirty)
• bwa (v. 0.7.12-r1039)

I have used the following polishing tools

• Racon (v. not available)
• Pilon (v. 1.18)
• Nanopolish (v. 0.8.3)

I have used the following tools to assess genome assembly characteristics

• ANI.pl (https://github.com/chjp/ANI)
• CheckM (v. 1.0.7)
• Prokka (v. 1.12)
• QUAST (v. 2.3)
• mummer (v. not available)

If you have any ideas or superior tools we have missed please let us know in the comments.

The Rearrangement Overview page describes the one or more breakpoints which make up a structural variant. A breakpoint is defined as a region or point where the sample sequence has altered from the reference sequence. Minimum interpretation is made of this data. One variant event can consist of one or multiple breakpoints. The Syntax (shown above the table) gives a detailed description of the variant and its location  (e.g. chr11:g.36585230_76606619del, a deletion of roughly 40Mb on chromosome 11). Syntax is based on HGVS mutation nomenclature recommendations [http://www.hgvs.org/rec.html]. 

http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview

Address of the bookmark: http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview

The pipeline is open-source and hosted in a public Bitbucket repository.

Address of the bookmark: https://tritexassembly.bitbucket.io/

https://github.com/tanghaibao/jcvi

More at https://github.com/tanghaibao/jcvi/wiki

Address of the bookmark: https://github.com/tanghaibao/jcvi

Randomness and probability are two differnet concepts: probaility is a measure (according to measure theory) which measures the randomness. Randomness is the object to be measured by probability. For example, probability is a mapping from randomness to the real number between 0 and 1. The similar examples are that the entropy measures the uncertanity; product of length and width measures the area of rectangle etc.

Please see “A mathematical theory of ability measure” by N. Kong ets for more examples to answer this question.

For smaller vertebrate genomes (~1 Gbp) chromosome scale assemblies can be achieved within 12h on high-end Desktop computers (Intel i7, 12 CPU threads, 128 GB RAM). Larger mammalian genomes (~3Gbp) can be processed within 15-18 h on server equipment (Xeon, 96 CPU threads, 1TB RAM).

Address of the bookmark: https://github.com/HMPNK/CSA2.6

We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on the genome-wide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs.

Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast, and will be invaluable in the study of complex genomes like that of human.

Address of the bookmark: http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

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

Careful encoding of graph entities allows odgi to efficiently compute and transform pangenomes with minimal overheads. odgi implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.

The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.

The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.

Address of the bookmark: https://github.com/pangenome/odgi