The next-generation sequencing included whole genome sequencing(WGS), transcriptome sequencing (whole cDNA sequencing, RNA-seq), digital gene expression sequencing (Tag-Seq), ChIP-Seq, and so on. And there are many sequencing platform to generate sequece, as well know Sanger/ABi(the frist generation), Solexa/illumina, SOLiD/ABi, 454/Roche. But thier sequence format is different, also they have different error type. High quality data is very important for further analysis or data mining. There are many pipeline for raw sequence quality analysis and control with few of process for reporting reads quality statistical details, trimming, filtering, and error correction. Please bookmarks them for the benefits of bioinformatics community.

https://code.google.com/p/biowiki/

https://code.google.com/p/ngs-pipeline/source/browse/#svn%2Ftrunk

NGSand Perl scripts https://code.google.com/hosting/search?q=NGS+perl&projectsearch=Search+projects

NGS and Python scripts https://code.google.com/hosting/search?q=NGS+Python&projectsearch=Search+projects

Address of the bookmark: https://code.google.com/hosting/search?q=bioinformatics&sa=Search

Under   a   NEPAD   initiative,   the   Biosciences   Eastern   and   Central   Africa   (BECA)  (www.biosciencesafrica.org) was established at ILRI. BECA consists of a hub, regional nodes, and  other affiliated laboratories and partner institutes. A state of the art joint Bioinformatics Platform  (www.becabioinfo.org), whose overall goal is to provide a coherent and powerful bioinformatics  infrastructure for use by all scientists in East and central Africa. The Platform goal requires both  physical and intellectual developments that together provide researchers with access to diverse  infrastructure in a wide­area network, thereby addressing four important aspects of bioinformatics: 

1) Science: bioinformatics tools for data integration and visualization, standardization of data  formats and data analysis strategies, and distribution of analysis tasks over local­ and widearea networks are in development; 

2)  Bioinformatics Support Facility: provides assistance and custom programming to projects  and those unable to establish a bioinformatics support function intrinsic to their project due  to shortage of qualified personnel or lack of funding; 

3) Hardware Platform: provide a powerful high performance computing platform capable of  handling the largest analysis needs for projects; 

4) Bioinformatics Training for East and central African scientists: While many Web­based  tools are available to the wet­lab researcher, the Web is not well suited for tasks beyond  single­sequence annotation. Researchers need to become productive in a server­based Unix  environment with its wealth of scripting and automation tools. Even at an entry­level, this  can be an intimidating task if proper guidance is not available.

International Centre of Insect Physiology and Ecology (ICIPE): ICIPE’s research focus is on insect biology, in order to improve the wellbeing of the peoples of the  tropics through insect science. There is a commitment to utilise contemporary science in order to  limit the impact of disease vectors, and agricultural pests. The understanding of the mechanisms  associated with behaviour (e.g. attraction and repellency) is crucial. ICIPE seeks to enhance its  bioinformatics capacity in order to support data from various EST projects designed to gain insights  into the insect ecology and plant pathogen interactions though studies of metabolic pathways  associated with production of all elochemicals. 

Long­term training activities:

Kenyatta University: An introductory course in Bioinformatics is offers to MSc Biotechnology  students. This comprises of 35 hours of lectures and practicals.

University of Nairobi: A centre for Biotechnology and Bioinformatics (CEBIB), which will offer  postgraduate training (diplomas, MSc and PhD) in areas of biotechnology and bioinformatics has  recently been launched. Other universities in Kenya, including Egerton, Maseno and the Jomo Kenyatta University of  Agriculture and Technology offer introductory courses to undergraduates in biomedical sciences. In addition, under the BECA platform MSc and PhD fellowships are being made available for  Bioinformatics students. ILRI is forging links with Universities in South Africa and the United  Kingdom to provide access to courses and training material. 

Research Interest and Activities:

The following are the present areas of research interest: 1. EST clustering 2. Genome sequencing and annotation 3. Functional genomics and proteomics (including key tropical pathogens) 4. Structural bioinformatics 5. Development of Bioinformatics Data Management Systems 6. Gene Mining 7. High Throughput Genotyping 8. Microarray data management and analysis 9. Metagenomics 10. Immunoinformatics 11. Host­pathogen interaction 12. High performance computing and grid development 13. Parasite transfection technologies 14. Cell cycle regulation 15. Population genetics 16. Vector genomics 17. Drug, vaccine and diagnostic target discovery

More at Web site and links:

http://www.ilri.cgiar.org/

http://www.icipe.org/    

http://www.uonbi.ac.ke/cebib

Address of the bookmark: http://www.bioapp.org/ewas/

Source: Life technologies

Address of the bookmark: http://www.lifetechnologies.com/global/en/home/communities-social/blog/blogs/sequencing-solutions-to-world-health.html?cid=social_blogseries_20130829_11098264

Address of the bookmark: http://omega.omicsbio.org/

So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are PacBio E. coli sample, ERS473430, ERS544009, ERS554120, ERS605484, ERS617393, ERS646601, ERS659581, ERS670327, ERS685285, ERS743109 and a deprecated PacBio E. coli data set. ONT data are acquired from the Loman Lab.

For a C. elegans PacBio data set (only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the HGAP3produces a 104Mb assembly with N50 1.61Mb. This dotter plot gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.

Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that minimap can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as MHAP and DALIGNER. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.

Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)

We start with an all against all comparison:

minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz

Then we can assemble

miniasm -f reads.fq reads.paf.gz > reads.gfa

Convert GFA to FASTA:

awk '/^S/{print ">"$2"\n"$3}' reads.gfa | fold > reads.fa

And then count how many contigs:

grep ">" reads.fa | wc -l

# Download sample PacBio from the PBcR website
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz | tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
# Install minimap and miniasm (requiring gcc and zlib)
git clone https://github.com/lh3/minimap && (cd minimap && make)
git clone https://github.com/lh3/miniasm && (cd miniasm && make)
# Overlap
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz
# Layout
miniasm/miniasm -f reads.fq reads.paf.gz > reads.gfa

Address of the bookmark: https://github.com/lh3/miniasm

Address of the bookmark: https://github.com/marbl/MashMap

https://github.com/ggonnella/rgfa

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103826/