Address of the bookmark: http://kakitone.github.io/finishingTool/

Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed an automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. Tested on both synthetic and real metagenome datasets, MetaBAT outperforms alternative methods in both accuracy and computational efficiency. Applying MetaBAT to an assembly from 1,704 human gut samples formed 1,634 genome bins (>200kb) in 3 hours, where 621 genome bins are >50% complete with <5% contamination from other species. Further analysis shows that the quality of these genome bins approaches manually curated genomes.

Address of the bookmark: https://bitbucket.org/berkeleylab/metabat

Address of the bookmark: http://ritchielab.psu.edu/software/phenogram-downloads

Our work is published in Scientific Reports:

Ye, C. et al. DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci. Rep. 6, 31900; doi: 10.1038/srep31900 (2016).

http://www.nature.com/articles/srep31900

The manual can be downloaded from:

https://github.com/yechengxi/DBG2OLC/raw/master/Manual.docx

To use precompiled versions,please go to:

https://github.com/yechengxi/DBG2OLC/tree/master/compiled

Address of the bookmark: https://github.com/yechengxi/DBG2OLC

It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer.^[1]

Address of the bookmark: https://en.wikipedia.org/wiki/FASTQ_format

https://github.com/jennomics/WorkflowPaper/blob/master/Genome%20Assembly%20and%20Annotation.md

Address of the bookmark: http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/

http://gmod.org/wiki/Diya

Address of the bookmark: https://sourceforge.net/projects/diyg/

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://www3.ocimumbio.com/

This course is organized with support from the IAP “Belgian Medical Genomics Initiative”, SymBioSys and the Genomics Core.

For inquiries, please email Ms Narcisse Opdekamp ( narcisse.opdekamp@uzleuven.be ).

More at >> http://gc.uzleuven.be/

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