Address of the bookmark: https://github.com/legumeinfo/gcv

1. “Development of bioinformatics methods for identifying novel secondary metabolites by genome mining” funded by DBT

JRF (P)/SRF (P) (One Position only)

Dr. Debasisa Mohanty Staff Scientist-VI deb@nii.res.in

Educational Qualifications: JRF (Project): M.Sc (Bioinformatics/ Biophysics/Biotechnology or any other stream of biological/physical sciences) or M.Tech. (Bioinformatics/Biotechnology/Computational Sciences) of M. Pharm.

SRF (Project): M.Sc (Bioinformatics/Biophysics/Biotechnology or any other stream of biological/physical sciences) or M.Tech. (Bioinformatics/Biotechnology/Computational Sciences) of M. Pharm with atleast 03 years of research experience.

Desirable Qualifications: Strong computer programming skills (in PERL/CGI/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment) and sufficient experience in computational analysis of biological/biochemical data.

The candidates must highlight their experience in programming and database development in their CV. Job description: Computational analysis of genomes and development of bioinformatics tools and software’s for sequence and structure based analysis of biosynthetic pathways.

Emoluments: The selected candidates will draw consolidated emoluments as per Institute Rules, depending upon qualifications & experience JRF (Project): Rs. 12,000/- per month plus 30% HRA SRF (Project): Rs. 14,000/- per month plus 30% HRA (*Candidates possessing qualifications as per latest DST OM, will be given revised scales).

More at http://www1.nii.res.in/sites/default/files/projectappointments-Dr.DebasisaMohanty-30April2015.pdf

IGIB Vacancy Details:
Total No. of Posts: 04
Name of the Posts:
1. Project Scientist (Biology): 02 Posts
2. Project Scientist (Bioinformatics): 01 Post
3. Sr Project Fellow (Ayurveda): 01 Post

Age Limit: Candidates age should be 35 years for post 1, 32 years for post 2

Educational Qualification: Candidates should have Ph.D/ Ph.D submitted in any branch of Biological Science/ Life Science for post 1, Ph.D/ Ph.D submitted in Bioinformatics for post 2, BAMS degree with one year internship for post 3.

Selection Process: Candidates will be selected based on their performance in interview.

How to Apply: Eligible candidates may send their application along with all relevant documents on or before 23-04-2015.

Important Dates:
Last Date for Receipt of Application for Post 1 & 2: 23-04-2015.
Date of Interview for Post 3: 27-04-2015.

For other details like pay scale, age relaxation, educational qualification, selection process, how to apply, etc., click on the link given below…

http://www.freejobalert.com/wp-content/uploads/2015/03/Notification-IGIB-Project-Scientist-SPF-Posts.pdf

http://www.igib.res.in/sites/default/files/27042015.pdf

What is Genome Assembly?

Genome assembly involves piecing together short DNA sequence reads generated by sequencing platforms (e.g., Illumina, PacBio, Oxford Nanopore) into longer, contiguous sequences called contigs. This can be performed as:

• De Novo Assembly: Without a reference genome.
• Reference-Guided Assembly: Using a reference genome to guide the assembly process.

Step 1: Preparing Your Data

Before starting the assembly, ensure that your raw sequencing data is high quality.

1. Input Data

   • Short Reads: Illumina sequencing generates short, accurate reads ideal for scaffolding.
   • Long Reads: PacBio and Nanopore sequencing provide long reads for resolving repetitive regions.

2. Quality Control (QC)
   Use tools like FastQC or MultiQC to assess the quality of your reads:

   fastqc reads.fastq multiqc .

   Look for issues like low-quality bases, adapter contamination, or overrepresented sequences.

3. Read Trimming and Filtering
   Trim low-quality bases and adapters using Trimmomatic or Cutadapt:

   trimmomatic PE reads_R1.fastq reads_R2.fastq trimmed_R1.fastq trimmed_R2.fastq \ ILLUMINACLIP:adapters.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36

Step 2: Choosing an Assembly Strategy

Select an assembly strategy based on your data type:

• Short-Read Assemblers:

  • SPAdes: Popular for microbial genomes.
  • Velvet: Fast for smaller genomes.

• Long-Read Assemblers:

  • Canu: Ideal for long-read datasets.
  • Flye: Versatile for small and large genomes.

• Hybrid Assemblers:

  • MaSuRCA: Combines short and long reads.
  • Unicycler: Optimized for bacterial genomes.

Step 3: Running the Assembly

3.1. SPAdes (Short-Read Assembly)

SPAdes is an excellent choice for small genomes, such as bacteria.

The output includes assembled contigs (contigs.fasta) and scaffolds (scaffolds.fasta).

3.2. Canu (Long-Read Assembly)

Canu is designed for high-error long reads from PacBio or Nanopore.

The output will be in canu_output/genome.contigs.fasta.

3.3. Hybrid Assembly with Unicycler

Unicycler combines short and long reads for improved assemblies.

Step 4: Assessing Assembly Quality

After assembly, evaluate its quality using the following tools:

1. QUAST
   QUAST generates assembly statistics, such as N50, genome size, and GC content:

   quast contigs.fasta -o quast_output

2. BUSCO
   BUSCO checks genome completeness by identifying conserved genes:

   busco -i contigs.fasta -o busco_output -l fungi_odb10 -m genome

3. Assembly Graph Visualization
   Visualize assembly graphs with Bandage:

   Bandage load assembly_graph.gfa

Step 5: Post-Assembly Steps

1. Polishing
   Improve assembly accuracy using tools like Pilon (for short reads) or Racon (for long reads).

   racon long_reads.fasta mapped_reads.sam contigs.fasta > polished_contigs.fasta

2. Scaffolding
   Link contigs into scaffolds using tools like SSPACE or Opera-LG if required.

3. Annotation
   Annotate the assembled genome using Prokka for prokaryotes or Maker for eukaryotes.

   prokka --outdir annotation_output --prefix genome contigs.fasta

Step 6: Sharing and Archiving

1. Submit to Public Repositories
   Share your assembly in databases like NCBI GenBank, ENA, or DDBJ.

2. Metadata Preparation
   Include detailed metadata for your submission, such as organism name, sequencing platform, and coverage.

Best Practices

• Always perform quality checks at each stage to ensure data integrity.
• Use multiple tools to cross-validate results when working with complex genomes.
• Document parameters and software versions for reproducibility.

Conclusion

Genome assembly is a powerful process that transforms raw sequencing data into a coherent representation of an organism’s genome. By following this step-by-step guide, you can successfully assemble genomes and uncover valuable biological insights. Whether you’re assembling a microbial genome or tackling the complexities of a eukaryotic genome, these tools and strategies will set you on the path to success.

We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.

For detailed instructions, please refer to the panHiTE tutorial.

Address of the bookmark: https://github.com/CSU-KangHu/HiTE

Project Description: Projects involves high throughput analysis of data mostly generated by massively parallel sequencing (RNA-Seq and small-RNA-Seq), microarrays and related platforms. We are looking for highly motivated and bright individuals interested in high-throughput cutting-edge data analyses methods in genomics (computational positions). Available positions: Applications are invited from suitable candidates in both, the Max Planck India Partner Program and the CRP Wheat Program for openings at the levels:

Post Name-Qualification-Salary:
Project assistant – Master’s – Rs. 14000
Project fellow (junior data analyst) – Masters + research experience – Rs. 16000
Research fellow (senior data analyst) – Masters + adequate research experience/desirable skill sets – Rs. 22000
Research Associated – PhD (< 1yr) /> 1 yr experience – Rs. 28000 / Rs. 32000
Essential qualification: MSc/MTech/PhD (or other suitable qualification) in discipline related to bioinformatics, computational biology, computer application (or equivalent)/ ‘Advance Post-Graduate Diploma in Bioinformatics’. Proficiency in one of the programming languages or statistics (proficient in R for example) is compulsory.
Desirable qualification: 1. Programming experiences in at least one low level language such as C/C++ and one scripting language such as Perl/Python/PHP and knowledge of SQL/MySQL. 2. Substantial experience in the linux or other unix environments. 3. Experience of working in projects on Bioinformatics, Genetics or Biological application areas/Computational and Statistical analysis (e.g. using R or Matlab). Experience in the field of genomics (NGS, microarrays, genome annotation), database development and management, software development, systems and network biology (or related fields) will be preferred.
SELECTION PROCEDURE FOR INDIAN INSTITUTE OF SCIENCE EDUCATION AND RESEARCH (IISER KOLKATA) – RESEARCH ASSOCIATE & MORE VACANCIES POST:

Candidates can apply on or before 30/04/2015
No Detailed information about the selection process is mentioned in the recruitment notification
HOW TO APPLY FOR RESEARCH ASSOCIATE & MORE VACANCIES IN INDIAN INSTITUTE OF SCIENCE EDUCATION AND RESEARCH (IISER KOLKATA):

Applications should contain CV along with brief description (maximum 1 page) of research conducted (highlighting skills and experience) till now. Applications should be sent by email to Shree P. Pandey, Department of Biological Sciences, IISER-Kolkata, Mohanpur Campus, West Bengal within 2 weeks. Interviews will be scheduled within 10 days of closing of applications. E-mail: sppiiserkol@gmail.com, sppandey@iiserkol.ac.in
For more details visit: http://www.iiserkol.ac.in/~sppandey

“It’s like a subway map,” she said, “but for DNA.”

That idea is exactly what Sequence Tube Map brings to life.

Instead of showing a genome as one long, confusing sequence, it displays different genomic paths like routes on a metro map. Where sequences are shared, the paths travel together. Where genetic variation occurs, they branch into different routes and may join again later.

This makes complex genome graphs much easier to explore.

Maya could now see how different haplotypes moved through the same genomic region and where they took different paths. She could even view sequencing reads mapped onto the graph, helping her understand what was happening in regions with genetic variation.

What once looked like a tangled network now looked like a journey.

Sequence Tube Map turns the complexity of graph genomes into a visual story—making it easier for scientists to see how DNA can take different routes.

Read more at  https://github.com/vgteam/sequencetubemap 

and

https://academic.oup.com/bioinformatics/article/35/24/5318/5542397

Demo at https://vgteam.github.io/sequenceTubeMap/

How To Apply: Eligible candidates required to report for the Interview at 9:00 A.M. sharp on 11-05-2015 (For Position Code No. 001 to 009) and 12-05-2015 (For Position Code No. 010 to 016). Candidates reporting after 10:00 A.M will not be allowed to attend the interview. Eligible candidates may appear before the Selection Committee for interview on the date and time mentioned above at CDRI, B.S. 10/1, Sector 10, Jankipuram Extension, Sitapur Road, Lucknow-226031. Eligible candidates must bring with them duly filled up application form (which can be downloaded from our website www.cdriindia.org), along with Original certificates as well as attested copies of certificates of examinations starting from matriculation, date of birth, caste certificate (in case of SC/ST/OBC) experience certificate, publication, if any and recent passport size photograph etc. Original documents are essential for verification of the particulars quoted by the candidate in the application form and candidate failed to produce original documents at the time of verification, shall not be allowed to attend the interview. Any request for relaxation in this regard shall not be entertained.
Detail of Interview: 11-05-2015
Age Limit: 28 Years

Instead of asking only whether two genes occur together across genomes, CORGIAS asks a deeper question: did these genes actually evolve together, repeatedly gaining or losing their presence in concert, or are they merely travelling together because of their inherited history? The framework introduces two complementary approaches, Ancestral State Adjustment (ASA) and the Simultaneous EVolution test (SEV), which incorporate evolutionary information into the search for gene associations. This distinction becomes increasingly important as genome sequencing continues to uncover enormous numbers of microbial genomes, many from organisms that have never been cultured and whose genes remain functionally mysterious. In this growing genomic landscape, simply finding a gene is no longer enough—we need clues about what that gene might be doing. CORGIAS approaches this problem almost like a genomic detective: rather than treating every association as evidence, it reconstructs the evolutionary story behind the association and asks whether the evidence survives that history. By doing so, it offers a way to separate coincidence from biological connection and potentially uncover functional relationships hidden within the vast microbial genomic landscape. The broader message is beautifully simple: genes do not evolve in isolation, and understanding what they do may require us to look beyond where they are today and reconstruct how they arrived there. Sometimes, the most important clue in a genome is not the gene itself, but the evolutionary journey it has taken.

More at https://academic.oup.com/nargab/article/7/4/lqaf182/8377910?