Features

• Circular and linear views of genomes

• Capable of drawing genomes up to 10 Mbp with 1000's of features and 100's contigs

• Smooth zooming down to the sequence level

• Easily generate features and plots directly form the sequence (e.g. ORFs, GC-content and GC-Skew)

• Save high resolution PNG maps up to 8000x8000px

• Fully documented API for interacting with CGView.js maps

Address of the bookmark: https://js.cgview.ca/

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.

PO Box: 21

DHARAMSHALA, DISTRICT KANGRA, HIMACHAL PRADESH – 176215

EMPLOYMENT NOTICE NO.: 02 / 2015

APPOINTMENT TO VARIOUS TEACHING, NON-TEACHING AND OTHER ACADEMIC STAFF POSITIONS

Applications in the prescribed form are invited from the eligible candidates for the following Teaching, Non-Teaching and other Academic Staff positions to be filled up on regular basis: Details of teaching positions:

15. School of Life Sciences

Computational Biology & Bioinformatics

1 (ST - 1) 2 (UR - 2)

Last Date of receipt of applications: 22ND JUNE, 2015

Advertisement:

http://www.cuhimachal.ac.in/download/2015/may-2015/emp-notice-eng/1.%20Employment%20Notice%20No.%2002-2015%20dated%2019.05.2015_for%20Website.pdf

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

Minimum qualifications: M.Sc. with minimum 55% for general and OBD Category (55% for SC/St/PH) in the above-mentioned subject areas (viz. Biotechnology, Life Sciences, Microbiology, Botany, Plant Sciences, Chemistry, Zoology, Animal Sciences, Fishery Sciences and any other relevant branches).

Preference will be given to those holding valid CSIR-UGC NET JRF. DBT-JRF, ICAR-JRF, ICMR-JRF and DST-INSPIRE Fellowship while NET/SLET/SET qualified and GATE qualified candidates (90 or above percentile) are also encouraged to apply. Reservations of seats: 15% for SC, 7.5% for ST, 27% for OBC (noncreamy layer) and 3% for Physically Handicapped as per statutory norms.

Selection Procedure: If the number of JRF and INSPIRE qualified candidates is more, selection will be based on interview of the JRF and INSPIRE qualified candidates only. The selected candidates may be registered for Ph.D. in any of the recognized Universities in India.

Application Procedure: Application should be sent in the prescribed application form (available on the IBSD website). The candidate should send the completed and signed form along with self attested copies of all supporting certificates and marksheets along with an application fee of Rs.300/- (For GEN/OBC/PH) & Rs.150/- for (SC/ST), for which a Demand Draft in favour of ‘Institute of Bioresources and Sustainable Development, payable at Imphal, Manipur, should be attached with the application form. Candidates are advised to provide their email ID and mobile number as they would be contacted electronically by the Institute. Duly filled applications (with ‘Application for IBSD PhD Programme’ super scribed on the envelope) should be sent to ‘The Director, Institute of Bioresources and Sustainable Development, Takyelpat, Imphal-795001, Manipur so as to reach on or before 6th of July, 2015. Applications send by email with scan copy of required enclosures will also be accepted and can be sent to director.ibsd@nic.in. However, in such instances, the application will be processed only after the receipt of the mailed hard copies.

Advertisement: http://ibsd.gov.in/jobs/phd_2015/IBSD_JRF_2015.pdf

Application Form : http://ibsd.gov.in/jobs/phd_2015/APPLICATION_FORM.pdf

“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/

Bioinformatics Textbook Track

Find more about Rosalind puzzle at http://rosalind.info/problems/list-view/?location=bioinformatics-textbook-track

I will provide solution of all the Rosalind problem with Perl for community.

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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?

The duly completed application on prescribed format along with copies of supporting documents must reach to: Office of the Dean (Research & Consultancy), Motilal Nehru National Institute of Technology, Allahabad-211004 on or before 03/07/2015.

The position is purely temporary and will be governed by the funding agency rules & service conditions of Office of the Dean (Research & Consultancy), MNNIT Allahabad.

For detail advertisement see: www.mnnit.ac.in/images/newstories/Advertisement_for_the_post_of_Research_Assistant_in_UPCST_Project_of_Biotechnology_Department.pdf