Address of the bookmark: https://github.com/sc932/ALE

To find repeats in a genome from 2 to 9 length using a Perl script, you can use the RepeatMasker tool with the "--length" option[0]. Here's a step-by-step guide:

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.

Key Findings:

1. Chromosomal Fusion and Its Molecular Signature:

   • The fusion site is located at 2q13–2q14.1 and is characterized by degenerate telomeric sequences appearing interstitially, indicating the historical head-to-head joining of ancestral chromosomes.
   • Despite being a signature of a past fusion event, these telomeric repeats are no longer functional and have undergone sequence degradation over time.

2. Extensive Duplications in the Surrounding Genomic Region:

   • The study identifies large-scale segmental duplications flanking the fusion site, with several of these regions duplicated and scattered across multiple chromosomes.
   • These duplications are predominantly located in subtelomeric and pericentromeric regions, suggesting their role in genomic instability and chromosomal evolution.

3. Paralogous Regions and Their Evolutionary Relationships:

   • A 168-kilobase (kb) segment near the fusion site has 98%–99% sequence identity with three regions on chromosome 9 (9pter, 9p11.2, and 9q13).
   • Another 67-kb region distal to the fusion site shows a high degree of homology to sequences in chromosome 22qter.
   • Additionally, a 100-kb segment exhibits 96% sequence identity with a region in chromosome 2q11.2.

4. Comparative Genomics and Evolutionary Implications:

   • By comparing the duplicated sequences and their arrangement in primates, the researchers traced the order of duplication events leading to their present distribution.
   • The presence of specific repetitive elements within these duplicated segments serves as evolutionary markers that help infer their historical rearrangements.
   • Some of these duplicated regions are associated with chromosomal inversion breakpoints, potentially contributing to evolutionary changes in primates.
   • Recurrent structural rearrangements in these regions have been linked to human chromosomal disorders.

Conclusions and Implications:

• The findings provide valuable insights into the structural evolution of human chromosome 2, which played a crucial role in human speciation.
• Understanding these segmental duplications and their evolutionary trajectories sheds light on genomic instability, which may contribute to human genetic diseases.
• The study highlights how large-scale chromosomal rearrangements, such as fusion and duplication, have influenced the evolutionary divergence of humans from other primates.

This research advances our understanding of human genome evolution and offers a foundation for studying the effects of structural variants in genetic disorders.

1. P.Pollastro, S.Rampone (2002). HS3D, a Dataset of Homo Sapiens Splice Regions, and its Extraction Procedure from a Major Public Database , International Journal of Modern Physics C, 13(8), 1105-1117. (please cite this paper)

2. P.Pollastro, S.Rampone (2003). HS3D: Homo Sapiens Splice Site Data Set , Nucleic Acids Research, 2003 Annual Database Issue.

Address of the bookmark: http://www.sci.unisannio.it/docenti/rampone/

https://cran.r-project.org/web/packages/chromoMap/index.html

Project Assistant (Level-II) job position in Central Food Technological Research Institute (CFTRI) on a temporary contractual basis in the research project (GAP 0469) funded by Science & Engineering Research Board (SERB), Government of India, New Delhi tenable at the Lipidomics Centre, CSIR-CFTRI, Mysore, Karnataka

Name of the Position :

Qualification : First class M. Sc. in Biochemistry/Microbiology/Genetics/ Bioinformatics with good academic record and preferably with experience in molecular biology techniques and basic knowledge of molecular biology and biological chemistry

Emoluments : Rs. 12,000/- per month (Consolidated)

Age Limit : The upper age limit for applying shall be 28 years (as on 22-5-2015), which is relaxed for candidates belonging to Scheduled Castes/Schedule Tribes, Women, Persons with Disabilities (PWD) and OBCs as per GoI norms.

How to apply

Eligible candidates may send their complete Bio-data with e-mail address/contact phone number along with attested copies of the necessary certificates through post to Prof. Ram Rajasekharan, Lipidomics Centre, Department of Lipid Science, CSIR-CFTRI, Mysore-570 020, Karnataka (email: ram@cftri.res.in) on or before 22.05.2015

More at http://www.cftri.com/pa_gap0469.html

Age Limit: 35

How to Apply for the AIIMS Life Science Job:

Interested applicants are asked to send out a detailed CV to Dr Ashok Sharma (aioncoaiims@gmail.com). Laboratory of Chromatin and also Cancer Epigenetics, Department of Biochemistry with the subject line “Application for Scientist-B position for MeitY project” latest by January 01st, 2021.
Complete Information of the year of passing, experience, marks, etc. ought to be mentioned in the CV Incomplete. applications will certainly be rejected Just shortlisted applicants will be called for interview. Chosen candidates will certainly be intimated by email/phone.
No TA/DA will certainly be paid for appearing in the interview.
Note, The institute reserved the right to fill up or not to fill up the post advertised.

Emoluments: Rs. 56,000/- plus 24 percent HRA

Eligibility:
2nd class Master’s Degree with a PhD in a pertinent subject (Bioinformatics) from.a recognized University
1st class Master’s degree in Life Sciences (Bioinformatics) from a recognized university OR.
Bachelor’s Degree in Engineering or-Technology with minimal 60% marks from a recognized University or equivalent.

Desirable Qualifications:
Experience in Bioinformatics/NGS data. Analysis/System Biology/Computer Science/ statistics with experience in Machine learning/Al project.
Experience of Deep learning applications in biological data ( image/text).
Proficient in Rf Python machine learning libraries.
Prior experience in the cancer-related project (ML-based) will be advantageous.
Experience with PyTorch/TensorFlow will certainly be very desirable.
Applicant should have strong scientific writing as well as. verbal abilities.
Papers in sci-indexed journals demonstrating ML skill sets.
Database handling will certainly be plus yet not required.

More detail at https://www.aiims.edu/images/pdf/recruitment/advertisement/biochem-16-12-20.pdf

Deciphering Life at the Molecular Level DNA sequencing is the key that opens the door to invaluable knowledge. By understanding the genes that make up Chilean species, we unravel the secrets of their evolution, their resistance and their adaptation to the environment. In a world where biodiversity faces constant threats, sequencing becomes crucial for the conservation and understanding of our natural heritage.

Involving Everyone: A Nationwide Effort The 1000 Genomes Chile Project is not just a task for scientists. It is a country-wide effort that seeks the participation of everyone: from citizens to the government to the private sector. We believe in the importance of sharing knowledge, involving society in the selection of species to sequence, in monitoring progress and in applying the results to preserve our environment.

Address of the bookmark: https://1000genomas.cl/