Computational methods used by the Shasta assembler include:

• Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
• Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

Experience Required: 3-5 years of experience

No of Positions : Multiple

Qualifications: Candidates with minimum qualification as M.Sc Bioinformatics with 3-5 years of experience in Life sciences R&D or Pharma Industry.

Ph.D candidates with research experience in Bioinformatics with publications in International journal and minimum 2 years of industry experience in clinical genomics will be preferred for this position.

Requirement:

1. Must have basic understanding of molecular biology and Genomics.

2. Experience in application development or must have expertise in programming using either of Perl/Python.

3. Experience in statistical programming using R/Bioconductor/Matlab.

4. Strong concept in statistical and mathematical modelling.

5. Experience in designing and developing the bioinformatics pipeline.

6. Must have minimum 2+ years of hands on experience in NSG data analysis such as RNA-Seq,Exome-Seq ,Chip-Seq and downstream analysis.

7. Knowledge in WGS ,WES, Targeted re-sequencing,GWAS and population genomics will be preferred.

8. Must have experience working on opensource software/Framework and commercial software for NGS data analysis and reporting.

9. Should be aware of handling big data and guiding team members on multiple projects simultaneously.

10. Should have experience coordinating with different groups of clinical research scientist for various project requirements.

11. Ability to work as team as well as independently with minimal support.

More at http://www.ocimumbio.com/careers1/

More at http://bioinfo.vanderbilt.edu/zhanglab/

Paper: http://science.sciencemag.org/content/early/2018/10/10/science.aau4832

More at https://www.wired.com/story/genome-hackers-show-no-ones-dna-is-anonymous-anymore/

More at http://biorg.cis.fiu.edu/

• Automatically improve draft assemblies
• Find variation among strains, including large event detection

Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:

• Single base differences
• Small indels
• Larger indel or block substitution events
• Gap filling
• Identification of local misassemblies, including optional opening of new gaps

More at https://github.com/broadinstitute/pilon/wiki

Address of the bookmark: https://github.com/broadinstitute/pilon/wiki

This book is suitable for classroom use as a general introduction to programming concepts.

More at http://tldp.org/LDP/abs/html/

Address of the bookmark: http://tldp.org/LDP/abs/html/

Note that the information on this page is targeted at end-users. For developers, the source code, building instructions and implementation/development resources are available on GitHub.

The Picard toolkit is open-source under the MIT license and free for all uses.

Enjoy!

Address of the bookmark: http://broadinstitute.github.io/picard/

into contiguous sequences (called contigs). One can use the sequences of different sequencing technologies either in a single assembly run (a true hybrid assembly) or by mapping one type of data to an assembly of other sequencing type (a semi-hybrid assembly (or mapping)) or by mapping a data against consensus sequences of other assemblies (a simple mapping).

The MIRA acronym stands for Mimicking Intelligent Read Assembly and the program pretty well does what its acronym says (well, most of the time anyway). It is the Swiss army knife of sequence assembly that I've used and developed during the past 14 years to get assembly jobs I work on done efficiently - and especially accurately. That is, without me actually putting too much manual work into it.

More at http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html

Address of the bookmark: http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html

1. Duplication level
2. kmer profile
3. per base GC content
4. per base N content
5. per base quality
6. per base sequence content
7. per sequence GC content
8. per sequence quality
9. sequence length distribution

More at http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/

Address of the bookmark: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/