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.

Bose Institute, Kolkata, invites applications from Indian Citizens for ONE (01) temporary position of Junior Research Fellow in the DBT sponsored project entitled, “Centre of Excellance (CoE) in Bioinformatics at Bose Institute”, running under Prof. Pinakpani Chakrabarti, Project Co-ordinatior, Bioinformatics Centre. The project is tenable upto 31.03.2017, but duration of the fellowship is one year only. The JRF will work with one of the faculty members of the center based on his / her motivation in any specific area on Bioinformatics.

Essential Qualification: 1st class M.Sc. / M.Tech degree in any stream of Chemical/ Biological Sciences with CSIR-UGC-NET-JRF / ICMR-JRF / DBT-JRF or CSIR-UGCNET- LS / GATE qualification.

Desirable qualification:

(i) Specialized knowledge in Organic / Physical chemistry.
(ii) Any exposure to research involving the small molecules (like drug) and / or protein structure determination or prediction.
(iii) Basic knowledge in computer programming, e.g. using FORTRAN, C, shell, perl etc.
(iv) Hands-on-experience on any of the following software : CHARMM/AMBER/NAMD/GROMACS,Gaussian/Gamess, Haddock/Autodock, Schrodinger etc. (or any other software serving similar purposes in molecular modeling)

Fellowship :

(i) Rs. 16,000/- p.m., plus admissible HRA & Medical Benefit for M.Sc. with CSIRUGC NET-JRF/ICMR-JRF/DBT-JRF or M.Tech. with CSIR-UGC NETJRF/
ICMR-JRF/DBT-JRF/CSIR-UGC NET-LS/GATE
(ii) Rs. 12,000/- p.m., plus admissible HRA & Medical Benefit for M.Sc. with CSIRUGC NET-LS/GATE

Age : Below 28 years as on the day on which the application is made (relaxable in case of SC/ST/OBC/WOMEN candidates only as per rule).

Interested and eligible candidates should apply on plain paper duly signed by them clearly mentioning the area of interest in research, possession of any desirable qualification (s) as mentioned above and quoting Advertisement No. on the envelop as well as application with complete Bio-data giving e-mail ID, Phone No. and details of qualification i.e. examination passed, year, division, percentage of marks, from Secondary onwards with attested copies of testimonials, addressed to the Registrar, Bose Institute, P-1/12, CIT Scheme VII-M, Kankurgachi, Kolkata-700054 on or before April 25, 2014.

The shortlisted candidates will be called for an interview. Applicants are advised to check our website for future updates.

Advertisement: www.boseinst.ernet.in/ADVT/14/p_2.pdf

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

Basic Galaxy Tutorial

• RNA-Seq tutorial based on Trapnell et al. (2012) Nature Protocols

In this tutorial we cover the concepts of RNA-Seq differential gene expression (DGE) analysis using a very small synthetic dataset from a well studied organism.

Advanced Galaxy Tutorial

• RNA-Seq (Advanced) Tutorial

In this tutorial we compare the performance of three statistically-based differential expression tools:

* CuffDiff

* EdgeR

* DESeq2

Advanced Command Line Tutorial

• Graphical Output with CummeRbund introduces some basic commands using the cummeRbund package of the R programming language

You will need to install R, RStudio and cummeRbund on your PC (explained in the Tutorial). You will learn how to produce graphical output from RNA-Seq analysis previously done using a Cuffdiff analysis.

Variant Detection

Basic Galaxy Tutorial

• Variant Detection tutorial

In this tutorial we cover the concepts of detecting small variants (SNVs and indels) in human genomic DNA using a small set of reads from chromosome 22.

Advanced Galaxy Tutorial

• Variant Detection (Advanced) Tutorial

In this tutorial we compare the performance of three statistically-based variant detection tools:

* SAMtools: Mpileup

* GATK: Unified Genotyper

* FreeBayes

Each of these tools takes as its input a BAM file of aligned reads and generates a list of likely variants in VCF format

Pipelines are for those who are comfortable with using the UNIX command line; and often allow more control over branching and iteration logic.

• WGS/exome GATK-based variant calling pipeline

This is a basic variant-calling and annotation pipeline developed at the Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne. It is based around BWA, GATK and ENSEMBL and was originally designed for human (or similar) data. The master branch is configured for WGS data; there is an exome branch configured for variant calling in exome data.

To run the pipeline you will need Rubra: https://github.com/bjpop/rubra. Rubra uses the python Ruffus library: http://www.ruffus.org.uk/.

Protocols

• Familial Variant Calling

In this protocol we discuss and outline the process of calling familial related mutations.

• Somatic Variant Calling

In this protocol we discuss and outline the process of identifying somatic variants or mutations.

Assembly

Basic Galaxy Tutorial

• Genome assembly tutorial

In this tutorial we carry out de novo assembly of a microbial genome. We have also written a De novo Genome Assembly for Illumina Data Protocol for a more generic description of the method.

Protocol

• De novo Genome Assembly for Illumina Data

In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes. Use our Genome assembly tutorial to learn a specific case of using Galaxy to carry out de novo assembly of a microbial genome.

Small RNAs

Basic Galaxy Tutorial

• Quality control for small RNA

This tutorial covers initial steps of the workflow for analysis of short RNA expression such as a quality control of the raw reads, processing of the raw reads for the subsequent analysis and initial quality assessment of the library.

ChIP Seq

Protocol

• ChIP-Seq

In this protocol we discuss ChIP-Seq: a method to analyze the interaction between proteins and DNA.

Amplicons

Protocol

• Amplicon Alignment

In this protocol we discuss and outline the process of aligning custom amplicons using primers for high precision.

Learn Galaxy

Introduction to Galaxy, for those who are very new to Galaxy.

Using Histories and Workflows, for those with some Galaxy knowledge.

The Galaxy project website has many tutorials and screencasts about using Galaxy and the tools, and developing new tools.

Address of the bookmark: https://genome.edu.au/wiki/Learn

No. of Post : 01

Department : Institute of Bioinformatics and Biotechnology

Qualification : (i) Good academic record as defined by the concerned University with at least 55% marks (or an equivalent grade in a point scale wherever grading system is followed) at the Master's degree level in a relevant subject from an Indian University, or an equivalent degree from an accredited foreign University. (ii) Besides fulfilling the above qualifications, the candidate must have cleared the National Eligibility Test (NET) conducted by the UGC, CSIR or similar test accredited by the UGC like SLET / SET. (iii) Candidates, who are, or have been awarded a Ph.D. degree in accordance with the University Grants Commission (Minimum Standards and Procedures for award of Ph.D. Degree) Regulations, 2009, shall be exempted from the requirement of the minimum eligibility condition of NET/SLET/SET. (iv) NET/SLET/SET shall not be required for such Master's Degree Programmes in disciplines for which NET/SLET/SET accredited test is not conducted.

Pay Band : Rs. 15,600 - Rs. 39,100/- with AGP of Rs.6,000

Application Fee : Application fees of Rs. 600/- (for Open Category) and Rs. 300/- (for candidates belonging to reserved categories),should be paid by the Challan (at Bank of Maharashtra/HDFC Bank)

More at http://collegecirculars.unipune.ac.in/sites/documents/Job%20Openings/ATNF161%20IBB_05.022019.pdf

No of vacancies: 01

Pay scale:Rs. 15600 – 39100 + 6600/-

Essential Academic Qualifications / Experience : Good academic record as defined by the concerned university with at least 55% marks (or an equivalent grade in a point scale wherever grading system is followed) at the Master's Degree level in a relevant subject from an Indian University, or an equivalent degree from an accredited foreign university.

Besides fulfilling the above qualifications, the candidate must have cleared the National Eligibility Test (NET) conducted by the UGC, CSIR or similar test accredited by the UGC like SLET/ SET.

Notwithstanding anything contained in sub-clauses (a) and (b) to this Clause, candidates, who are, or have been awarded a Ph.D. Degree in accordance with the University Grants Commission (Minimum Standards and Procedure for Award of Ph.D. Degree) Regulations, 2009, shall be exempted from the requirement of the minimum eligibility condition of NET/SLET/SET for recruitment and appointment of Assistant Professor or equivalent positions in Universities/Colleges/Institutions.

NET/SLET/SET shall also not be required for such Masters Programmes in disciplines for which NET/SLET/SET is not conducted.

Apply online: http://www.psc.cg.gov.in/htm/OA_ME2014.html

Last Date for Online Registration: 22/05/2014

For more details: http://www.psc.cg.gov.in/pdf/Advertisement/ADV_ME2014.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.

Address of the bookmark: http://www.compbio.dundee.ac.uk/jpred4/

More at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369

Address of the bookmark: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369