Bioinformatics

High-throughput and high-dimensional data analysis

Microbiome data analysis (Main focus)

Next-generation and third-generation sequencing data analysis for genomics

Gene expression data analysis

Machine learning for biological data

Biomarkers identification

Database and web-application for biological data

More at
https://sites.google.com/mail.kmutt.ac.th/kanthida-k/home?authuser=0

There are several jobs opening in bioinformatics all around the world, but many of them do not get proper attention due to lack of advertisements, or social connectivity. This bookmark is created for an academic, non-academic, scientists and budding researchers to help and updates the bioinformatics/computational biology jobs links of all know websites around the world.

I also love to stream the live bioinformatics or Computational biology jobs updates using Twitter https://twitter.com/search?q=bioinformatics%20jobs&src=typd

Find out here about exciting job opportunities in the life sciences.

Please add well known bioinformatics jobs websites below in comment section.

Address of the bookmark: http://www.nature.com/naturejobs/science/jobs?utf8=%E2%9C%93&q=bioinformatics&where=&commit=Find+Jobs

Address of the bookmark: https://www.biostarhandbook.com/

the study of the sequence-structure relationship
(application -> structure prediction)
the study of the structure-function relationship
(application -> function prediction)

Therefore, anything related to the configuration (sequence) and conformation (structure) in atomic systems of proteins and nucleic acids, and the interaction of these with other elements (function) is of our major interest.

Lab page @ http://melolab.org/mbl/

1. NCBI (National Center for Biotechnology Information):

   • NCBI provides various databases related to genetic information, including 16S rRNA sequences.
   • You can access the 16S ribosomal RNA sequences from NCBI's Nucleotide database (https://www.ncbi.nlm.nih.gov/nucleotide/).
   • Perform a search using keywords like "16S rRNA" or specific bacterial names to find relevant sequences.
   • You can download sequences individually or in batches using the provided tools.

2. GreenGenes:

   • GreenGenes is a widely used 16S rRNA gene sequence database.
   • You can access it at http://greengenes.secondgenome.com/.
   • GreenGenes provides precompiled databases for various purposes, including classification, alignment, and phylogenetic analysis.

3. SILVA:

   • SILVA (https://www.arb-silva.de/) is another comprehensive database for ribosomal RNA (rRNA) sequences.
   • It covers not only 16S rRNA but also other ribosomal RNA sequences.
   • SILVA provides precompiled databases for various purposes, including taxonomic classification and alignment.

4. Ribosomal Database Project (RDP):

   • RDP (http://rdp.cme.msu.edu/) is a curated database that offers 16S rRNA sequences.
   • It provides tools for sequence analysis and classification.
   • You can download sequences and taxonomy information from their website.

5. QIIME (Quantitative Insights Into Microbial Ecology):

   • QIIME (https://qiime2.org/) is a widely used bioinformatics platform for microbiome analysis.
   • It provides tools for analyzing microbial communities, including processing 16S rRNA sequences.
   • QIIME often includes its own preprocessed 16S rRNA databases that can be used for analysis within the platform.

Before downloading any database, make sure to read the terms of use and citation requirements, as some databases may have specific usage policies. Additionally, consider the compatibility of the database with your analysis pipeline and software tools.

NCBI 16s RNA database location ftp://ftp.ncbi.nih.gov/blast/db/16SMicrobial.tar.gz

1 Scientist E 3

50,000/-

M.Sc. in Life sciences or Plant Sciences or Biotechnology or Microbiology or Bioinformatics or Ph.D. from a recognized university in any of above subject.

Minimum 8 Yrs. of experience after M.Sc. or 5 Yrs. of experience after Ph.D. in responsible position of work in R & D in the area of genomics/ conservation biotechnology/bioinformatics/Planning/Scientific Administration in Science and technology organization. Highly qualified in the area of modern biology, as evidenced through research experience and proven ability to carry out work in the area of conservation biotechnology. Age limit not exceeding 40yrs.

2 Scientist B 6

30,000/-

M.Sc. in Life sciences or Plant Sciences or Biotechnology or Microbiology or Bioinformatics or Ph.D. from a recognized university in any of above subject shall be preferred.

Minimum 3 Yrs. of experience after M.Sc. in responsible position of work in R & D in the area of genomics/ conservation biotechnology/ bioinformatics /Planning/Scientific Administration in Science and technology organization. Highly qualified in the area of modern biology, as evidenced through research experience and proven ability to carry out work in the area of conservation biotechnology. Age limit not exceeding 35yrs.

The positions are purely on contractual basis for 11 months. Interested candidates can apply online in specified format available at "http://leogen.in/recruit/" The last date of applying is 24th December, 2013. Applications must be submitted online only. Applications submitted in any other format except online prescribed performa will be rejected. Candidates in service must apply through proper channel. Candidates will be required to provide original documents along with duly filled and signed application Performa, as and when called for interview.

For more details please visit the website URL : http://leogen.in/recruit

Average read length is around 8,500 base pairs in release dataset. Best thing about this, there is not much effect on cost and quality of data.

You can also check following pages for publications on long reads and more:

http://www.illumina.com/services/long-read-sequencing-service.ilmn

http://blog.basespace.illumina.com/2013/07/22/first-data-set-from-fasttrack-long-reads-early-access-service/

Translational recoding.

RNA editing.

Evolution of the genetic code and translation.

More at http://lapti.ucc.ie/research.html

Lab page http://lapti.ucc.ie/index.html

Usage: perl run_rcorrector.pl [OPTIONS]
OPTIONS:
	Required
	-s seq_files: comma separated files for single-end data sets
	-1 seq_files_left: comma separated files for the first mate in the paried-end data sets
	-2 seq_files_right: comma separated files for the second mate in the paired-end data sets
	-i seq_files_interleaved: comma sperated files for interleaved paired-end data sets
	Optional
	-k INT: kmer_length (<=32, default: 23)
	-od STRING: output_file_directory (default: ./)
	-t INT: number of threads to use (default: 1)
	-trim : allow trimming (default: false)
	-maxcorK INT: the maximum number of correction within k-bp window (default: 4)
	-wk FLOAT: the proportion of kmers that are used to estimate weak kmer count threshold, lower for more divergent genome (default: 0.95)
	-ek INT: expected number of kmers; does not affect the correctness of program but affects the memory usage (default: 100000000)
	-stdout: output the corrected reads to stdout (default: not used)
	-verbose: output some correction information to stdout (default: not used)
	-stage INT: start from which stage (default: 0)
		0-start from begining(storing kmers in bloom filter) ;
		1-start from count kmers showed up in bloom filter;
		2-start from dumping kmer counts into a jf_dump file;
		3-start from error correction.

Address of the bookmark: https://github.com/mourisl/Rcorrector/