<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/19560?offset=300 https://bioinformaticsonline.com/blog/view/44722/step-by-step-guide-to-running-genome-assembly Fri, 13 Dec 2024 11:35:55 -0600 https://bioinformaticsonline.com/blog/view/44722/step-by-step-guide-to-running-genome-assembly <![CDATA[Step-by-Step Guide to Running Genome Assembly]]> Genome assembly is a critical process in bioinformatics, enabling the reconstruction of an organism's genome from short DNA sequence reads. Whether you’re working on a new microbial genome or a complex eukaryotic organism, this guide will walk you through the steps of genome assembly using state-of-the-art tools and best practices.

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.

spades.py -1 trimmed_R1.fastq -2 trimmed_R2.fastq -o spades_output

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.

canu -p genome -d canu_output genomeSize=4.7m -nanopore-raw reads.fastq

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.

unicycler -1 trimmed_R1.fastq -2 trimmed_R2.fastq -l long_reads.fastq -o unicycler_output

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.

]]> Abhi https://bioinformaticsonline.com/blog/view/44775/genomic-architecture-surrounding-the-fusion-site-of-human-chromosome-2 Tue, 04 Mar 2025 12:26:29 -0600 https://bioinformaticsonline.com/blog/view/44775/genomic-architecture-surrounding-the-fusion-site-of-human-chromosome-2 <![CDATA[Genomic architecture surrounding the fusion site of human chromosome 2]]> The article "Genomic Structure and Evolution of the Ancestral Chromosome Fusion Site in 2q13–2q14.1 and Paralogous Regions on Other Human Chromosomes (https://pmc.ncbi.nlm.nih.gov/articles/PMC187548/)" explores the genomic architecture surrounding the fusion site of human chromosome 2. This fusion event is a key evolutionary marker distinguishing humans from other great apes, as humans have 46 chromosomes while chimpanzees, gorillas, and orangutans possess 48. The fusion occurred through an end-to-end joining of two ancestral chromosomes, which remain separate in nonhuman primates.

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.

]]> LEGE https://bioinformaticsonline.com/blog/view/45267/when-thousands-of-bacterial-genomes-become-one-giant-map Thu, 27 Aug 2026 10:56:47 -0500 https://bioinformaticsonline.com/blog/view/45267/when-thousands-of-bacterial-genomes-become-one-giant-map <![CDATA[When Thousands of Bacterial Genomes Become One Giant Map]]> Imagine trying to understand a city by studying just one house.

You might learn a lot about that house—the rooms, the doors, the furniture—but you would miss the bigger story: the streets, the neighborhoods, and all the ways the city changes from one place to another.

Something similar happens when scientists study bacterial genomes one at a time.

Over the past decade, researchers have collected thousands of bacterial genomes. These genomes contain an enormous amount of information about how bacteria survive, adapt, and evolve. But comparing thousands of individual genomes can quickly become a computational maze.

That is where PANORAMA enters the story.

Developed by Jérôme Arnoux and colleagues, PANORAMA is a computational tool designed to explore bacterial pangenomes—the complete collection of genetic possibilities found across a species or group of related organisms. Instead of looking at every genome as an isolated object, the approach represents their shared and variable genetic features as a graph.

Think of this graph as a giant subway map.

Some stations appear on almost every route. These represent genes that are highly conserved. Other stations appear only on certain routes, representing genes that some bacteria possess while others do not. By looking at the entire network, scientists can begin to see not just *what genes exist*, but how they are organized and how biological systems are distributed across populations.

The researchers put PANORAMA to the test using 941 genomes of Pseudomonas aeruginosa, a bacterium important in human health. They used the tool to investigate biological systems, including bacterial defense mechanisms against viruses known as bacteriophages. They then expanded the analysis to more than 6,000 genomes from four Enterobacteriaceae species.

The result is more than a faster way to process data.

PANORAMA allows researchers to ask a bigger question: What can an entire microbial species do, genetically speaking?

By comparing pangenomes, the researchers could identify systems shared between species as well as distinctive features. They could also find recurring genomic locations where genetic material is inserted—clues that may reveal common evolutionary processes.

And this is perhaps the most exciting part of the story.

Every bacterial genome is like a page in a huge evolutionary book. Until recently, reading thousands of those pages together was difficult. PANORAMA provides a way to turn those pages into a map, allowing scientists to see patterns that might disappear when each genome is studied separately.

The study, published in PLOS Computational Biology in July 2026, presents PANORAMA as a foundation for large-scale comparative pangenomics. The software and accompanying analysis resources are openly available, giving other researchers the opportunity to explore microbial diversity themselves.

So the story is not really about one bacterium or one genome.

It is about changing the way we look at life.

Instead of asking, “What is inside this genome?”, scientists can increasingly ask, “What is the full genetic landscape of this species—and how did it become this way?”

And sometimes, when you stop looking at one house and finally see the whole city, the most interesting discoveries are hiding in the streets between them.

Read more at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013856

]]> LEGE https://bioinformaticsonline.com/blog/view/45303/the-tree-that-learned-to-read-genomes Fri, 11 Sep 2026 00:51:02 -0500 https://bioinformaticsonline.com/blog/view/45303/the-tree-that-learned-to-read-genomes <![CDATA[The Tree That Learned to Read Genomes]]> Imagine trying to reconstruct a family tree when many family photographs are missing, some names are unclear, and different relatives remember the past differently. This is similar to the challenge scientists face when trying to understand how organisms are related. Their family records are genomes, and the family tree is a phylogenetic tree. With modern sequencing technologies, scientists now have access to thousands of genomes, but comparing them can be difficult, especially when genomes are incomplete or poorly annotated.

A recent study by Webster and Chapman introduces Buscogeny (https://github.com/Jwebster89/Buscogeny), an open-source bioinformatics pipeline designed to make this process easier. The tool uses BUSCO (Benchmarking Universal Single-Copy Orthologs), which identifies conserved genes that are expected to occur as single copies in particular groups of organisms. These genes act like evolutionary landmarks, allowing researchers to compare genomes and investigate their relationships.

Buscogeny brings several steps of phylogenomic analysis into one workflow. It identifies BUSCO genes, extracts and aligns their sequences, removes unreliable regions, combines information from multiple genes, and uses the resulting data to construct a phylogenetic tree. It also provides quality-control information, helping researchers identify genomes that may be too incomplete for reliable analysis.

The authors demonstrate the usefulness of Buscogeny with bacterial and fungal genomes, including Alternaria. This is particularly valuable because fungal genomes can vary greatly in quality and annotation. Ultimately, Buscogeny helps transform massive amounts of genomic data into an understandable evolutionary story. Each DNA sequence becomes a clue, each conserved gene becomes a landmark, and together these clues reveal a picture of how life is connected.

More at https://link.springer.com/article/10.1007/s10123-025-00752-6?

]]> BioStar https://bioinformaticsonline.com/news/view/2645/dna-bending-propensity-in-the-presence-of-base-mismatches-implications-for-dna-repair Mon, 19 Aug 2013 16:01:44 -0500 https://bioinformaticsonline.com/news/view/2645/dna-bending-propensity-in-the-presence-of-base-mismatches-implications-for-dna-repair <![CDATA[DNA Bending Propensity in the Presence of Base Mismatches: Implications for DNA Repair]]> Understanding how the human body recognizes damaged DNA and initiates repair fascinates Michael Feig, professor of biochemistry and molecular biology at Michigan State University. Feig studies the proteins MutS and MSH2-MSH6, which recognize defective DNA and initiate DNA repair. Natural DNA repair occurs when proteins like MutS (the primary protein responsible for recognizing a variety of DNA mismatches) scan the DNA, identify a defect, and recruit other enzymes to carry out the actual repair.

Results from computer simulations show that it is energetically less expensive to bend mismatch-containing, defective DNA (G:T, C:C, C:T, G:A, G:G, T:T, A:A, A+:C) vs. non-defective DNA (containing A:T or G:C base pairs). DNA repair mechanisms likely take advantage of this feature to detect defective DNA based on an increased bending propensity.

http://www.tacc.utexas.edu/news/feature-stories/2013/how-dna-repair-helps-prevent-cancer

http://pubs.acs.org/doi/abs/10.1021/jp403127a

]]> Jitendra Narayan https://bioinformaticsonline.com/researchlabs/view/4656/pandey-lab Fri, 20 Sep 2013 13:19:18 -0500 <![CDATA[Pandey Lab]]> The Pandey Lab at Johns Hopkins University is a Systems Biology lab that combines molecular biology, analytical chemistry and computational biology with various "Omics" technologies including genomics and proteomics to understand signaling pathways and to identify therapeutic targets and biomarkers in a number of cancers.

More at http://pandeylab.igm.jhmi.edu/

http://scholar.google.com/citations?user=OhuG0FcAAAAJ&hl=en

]]> https://bioinformaticsonline.com/opportunity/view/13338/protein-function-annotation-and-machine-learning-upmc-paris-france Sat, 02 Aug 2014 01:22:52 -0500 <![CDATA[Protein function annotation and machine learning - UPMC - Paris, France]]> Protein function annotation and machine learning - UPMC - Paris, France

Job Description: We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of paris.

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

Title: A novel integrative platform for large scale protein annotation that exploits a multitude of diversified probabilistic models in several protein signature databases.

We propose a novel integrated approach for large scale protein annotation that will exploit an unprecedented amount of genomic data as well as sophisticated machine learning techniques and combinatorial optimization approaches taking advantages of High Performance Computing (HPC) environments. The idea is to uncover as much as possible the evolutionary processes of protein sequences that took place throughout the whole tree of life and that affected the evolution of a protein family. We have already demonstrated in a previous work that the problem of functional annotation is inherent to the ability of uncovering such paths. Now, we shall extend this approach to large scale genome annotation by considering 11 different protein databases, constituted by about 10^9 protein sequences, and by producing a large pool of diversified probabilistic models coding for about 10^7 evolutionary protein pathways. Such models will be used to search for specific domains in genomes to be annotated. Our previous methodology needs to be fundamentally improved to deal with this large amount of biological data. In this project, we shall work on the algorithms to reduce the space of models and the search complexity, and we shall implement some important algorithmic changes towards the realization of a powerful integrated annotation tool.

Where: This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

Start date: September 1st, 2014
Contact Person: Alessandra Carbone
Contact: alessandra.carbone@lip6.fr

]]> https://bioinformaticsonline.com/pages/view/18749/list-of-biotechnology-research-laboratory-university-and-centre-in-india Thu, 30 Oct 2014 12:34:34 -0500 https://bioinformaticsonline.com/pages/view/18749/list-of-biotechnology-research-laboratory-university-and-centre-in-india <![CDATA[List of Biotechnology Research Laboratory, University, and Centre in India]]> DEPARTMENT OF BIOTECHNOLOGY

INDIAN COUNCIL OF MEDICAL RESEARCH (ICMR)

COUNCIL OF SCIENTIFIC & INDUSTRIAL RESEARCH (CSIR)

 

CITYWISE

City Wise Llist of Govt. and Non Govt. Research institutions in India -

Ahmedabad

Allahabad

Bangalore

Barrackpore (W. B.)

· Central Inland Capture Fisheries Research Institute

Bhopal

Bhubaneswar

Calcutta

Chandigarh

Chennai (Madras)

Delhi

Dehradun

Dirang (Arunachal Pradesh)

Durgapur

Eluru (A. P.)

Gandhi Nagar

Goa

Hyderabad

Indore

Jammu

Jhansi (U. P)

Jodhpur (Rajasthan)

Kanpur

Kasaragod

Kharagpur

Kochi

Lucknow

Mathura

Mumbai (Bombay)

Palakkad

Palampur (H.P.)

Peechi

Pilani

Puttur (Karnataka)

Pune

Roorkie (U. P.)

Trivandrum (Thiruvananthapuram)

Varanasi (U. P.)

· Indian Institute of Vegetable Research

 

UNIVERSITIES/COLLEGES UNDER UGC

DST

•  Agharkar Research Institute, Pune

•  Birbal Sahni Institute of Palaeobotany, Lucknow

•  Bose Institute, Kolkata

•  Indian Association for the Cultivation of Science, Kolkata

•  International Advanced Research Centre for Powder Metallurgy and New Materials,
...esr ...Hyderabad

•  Indian Institute of Astrophysics, Bangalore

•  Indian Institute of Geomagnetism, Mumbai

•  Indian Institute of Tropical Meteorology, Pune

•  Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore

•  National Accreditation Board for Testing & Calibration Laboratories, New Delhi

•  Raman Research Institute, Bangalore

•  S.N. Bose National Centre for Basic Sciences, Kolkata

•  Sreechitra Tirunal Institute for Medical Sciences & Technology, Thiruvananthapuram

•  Technology Information, Forecasting & Assessment Council (TIFAC), New Delhi

•  Vigyan Prasar, New Delhi

 

Indian Council of Agricultural Research

  • National Bureaue of Soil Survey & Land Use Planning (NBSSLUP)
  • Central Research Institute for Jute & Allied Fibres (CRIJAF)
  • National Bureaue of Agriculturally Important Micro-Organisms (NBAIMO
  • Central Research Institute for Dryland Agriculture
]]> Jit https://bioinformaticsonline.com/opportunity/view/23122/candidates-required-in-bioinformatics-and-genomics-uk-only Fri, 03 Jul 2015 08:22:41 -0500 <![CDATA[Candidates required in Bioinformatics and Genomics UK ONLY]]> I have various permanent positions available based in London, Manchester, Herftfordshire, Oxford and Belfast, as well as other areas throughout the UK.

If you are looking for a new opportunity and have skills within any sector of Bioinformatics with an IT skill then I would love to hear from you. I have various exciting opportunities from programmers to researchers to scientists.

Call me now on 01772 278050 or email me your cv and requirements and I will call you back dareen.evans@itworkshealth.co.uk

]]> https://bioinformaticsonline.com/researchlabs/view/26828/bioinfolab Fri, 25 Mar 2016 11:05:35 -0500 <![CDATA[BioinfoLab]]> Laboratory of Statistics and Computational tools for Bioinformatics

The Laboratory of Statistics and Computational tools for Bioinformatics (BioinfoLab) is hosted at the Istituto per le Applicazioni del Calcolo "Mauro Picone" - CNR . The laboratory has been officially opened in 2012 with the support of Programma Operativo Nazionale "Ricerca e Competitività" 2007-2013 (PON "R&C"), and it incorporates several expertise and research activities started since 2007, and supported by several CNR projects. Main interest of BioinfoLab is to develop novel statistical methods and computational tools for the analysis of high dimensional data arising from "Multi-omics" applications. In particular, current activities involve the analysis of ChIP-seq and RNA-seq experiments.

More at http://bioinfo.na.iac.cnr.it/BioinfoLab/index.html

]]>