https://bioinformatics.kmutt.ac.th/about.html

Duangrudee Tanramluk (Ajarn Wi) uses computational biology and machine learning to tackle the key to drug design problems via MANORAA webserver.

https://mb.mahidol.ac.th/en/bioinformatics/

https://graduate.mahidol.ac.th/inter/

This international Doctorate programme is designed to further broaden students’ knowledge in Bioinformatics and Molecular Biology to their maximum capability. 

http://www.mbb.psu.ac.th/programmes/phd

Ph.D. program in Bioinformatics and Computational Biology is a joint effort of the Faculty of Science and Faculty of Medicine, Chulalongkorn University. The program has study plans for both applicants who hold a bachelor’s degree and applicants who hold a master’s degree in any related fields of study.

http://www.bioinfo.sc.chula.ac.th/ph-d-program-specialization/

Additional detail 

https://www.biotec.or.th/en/index.php/research/research-units/genome-technology-research-unit

https://tbrcnetwork.org/labtbrc/index.php/bioinformatics-and-chemoinformatics/

https://genomicsthailand.com/Genomic/home

Address of the bookmark: https://bioinformatics.kmutt.ac.th/

Like other indel callers, Scalpel works by performing de novo assembly of regions of interest, so that misalignment to the reference genome cannot obscure the presence of an insertion or deletion. Scalpel's innovation is to repeatedly check its assembly before comparing to the reference genome, to account for simple sequence repeats that are a regular source of error in indel calling. When Scalpel assembles an exon, it collects reads that map to that exon (including partial matches), splits them into k-mers, and creates a de Bruijn graph to span the exon; however, if it detects repeats in the map, it iteratively increases the size of the k-mers by one base until the repeats are eliminated. This ensures that the final assembly of the exon is highly accurate while minimizing compute time.

The Cold Spring Harbor team's validation of Scalpel, published over the weekend in Nature Methods, compares Scalpel's performance on a live whole exome against HaplotypeCaller and SOAPindel. The donor is an individual with serious neurological disorders, which may be linked to a high incidence of indels. One thousand indels from this individual's exome, called by one or more of the informatics pipelines, were selected for focused resequencing. This resequencing revealed a 77% true positive rate for Scalpel calls, dramatically better than the rates for either of the competing tools; Scalpel performed especially well with indels longer than five base pairs, a traditional weak point for indel callers.

Finally, the authors demonstrate Scalpel's use on a large set of genetic data from nearly 600 families who donated samples to the Simons Simplex Collection, a project of the Simons Foundation Autism Research Initiative. Scalpel found a very high enrichment for indels in children affected by autism, compared with their unaffected siblings, a pattern that persisted even after excluding common variants.

Bioinformatics : https://www.linkedin.com/jobs/view/2681785372/

Engineer: https://www.linkedin.com/jobs/view/2681796993/

1. SPAdes: An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.

2. ABySS: A parallelized assembler for large genomes that uses de Bruijn graphs.

3. Velvet: Another de Bruijn graph-based assembler optimized for short-read sequencing data.

4. SOAPdenovo: A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.

5. MaSuRCA: A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.

6. Canu: A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.

7. Flye: A long-read assembler suitable for bacterial and small eukaryotic genomes.

8. SMARTdenovo: An assembler designed for long reads, particularly suited for PacBio data.

9. SPAdes Long Read (SPAdesLR): An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.

10. Minia: An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.

11. Unicycler: A hybrid assembler that combines short and long reads for circular bacterial genome assembly.

12. wtdbg2: A de Bruijn graph assembler for long reads, efficient for very large genomes.

13. Shasta: A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.

14. Sparc: An assembler designed to handle noisy long reads from Nanopore sequencing.

15. CANA: An assembler for metagenomic data, particularly for complex and diverse microbial communities.

16. Ra Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.

Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.

Address of the bookmark: https://biokit.readthedocs.io/en/latest/index.html

You can find some useful open source bioinformatics codes for your analysis work. You can use the left bar options to filtere out or narrow down your search result. This webpage can be an useful resource for a beginners bioinformatician as it contain several bioinformatics basics script that are commonly used by biological programmers and biologist.

Stand on the slumped, dandruff-covered shoulders of millions of computer nerds. _/\_

Enjoy the code and research work.

http://code.ohloh.net/search?s=bioinformatics

Address of the bookmark: http://code.ohloh.net/search?s=bioinformatics

Qualifications:
MSc degree in computer science, engineering, genetics or similar field with a strong emphasis on computational methods.

Deadline
01.08.2015

Address of the bookmark: http://ocw.mit.edu/courses/index.htm

For those familiar with Trellis Display, faceting in ggplot, or the notion of small multiples, Trelliscope provides a scalable way to break a set of data into pieces, apply a plot method to each piece, and then arrange those plots in a grid and interactively sort, filter, and query panels of the display based on metrics of interest. With Trelliscope, we are able to create multipanel displays on data with a very large number of subsets and view them in an interactive and meaningful way.

Address of the bookmark: http://deltarho.org/docs-trelliscope/#introduction