<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/34567?offset=420 https://bioinformaticsonline.com/bookmarks/view/43799/kast Wed, 23 Feb 2022 08:28:36 -0600 https://bioinformaticsonline.com/bookmarks/view/43799/kast <![CDATA[KAST]]> Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.

Address of the bookmark: https://github.com/martinjvickers/KAST

]]> Neel https://bioinformaticsonline.com/bookmarks/view/44223/ale-assembly-likelihood-estimator Wed, 08 Mar 2023 01:39:33 -0600 https://bioinformaticsonline.com/bookmarks/view/44223/ale-assembly-likelihood-estimator <![CDATA[ALE: Assembly Likelihood Estimator]]> Just import the assembly, bam and ALE scores. You can convert the .ale file to a set of .wig files with ale2wiggle.py and IGV can read those directly.  Depending on your genome size you may want to convert the .wig files to the BigWig format.

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

]]> BioStar https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome Thu, 31 Aug 2023 02:43:28 -0500 https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome <![CDATA[Steps to find all the repeats in the genome !]]>

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:

  1. Install RepeatMasker: First, you need to install RepeatMasker on your system. You can download it from the RepeatMasker website[0].
  1. Prepare the genome sequence: Make sure you have the genome sequence in a FASTA file format. Let's assume the file is named "genome.fasta".

./RepeatMasker -pa <number_of_processors> -nolow -norna -no_is -div <divergence_value> -lib RepeatMaskerLib.embl -gff -xsmall -small -poly -species <species_name> -dir <output_directory> -length <min_length>-<max_length> genome.fasta

Replace the following placeholders with appropriate values:

  • <number_of_processors>: The number of processors/threads you want to use for parallel processing.
  • <divergence_value>: The divergence value for the species you are analyzing. You can find divergence values for different species in the RepeatMasker documentation[0].
  • <species_name>: The name of the species you are analyzing.
  • <output_directory>: The directory where you want the output files to be saved.
  • <min_length> and <max_length>: The minimum and maximum lengths of the repeats you want to find (in this case, 2 and 9).
  1. Analyze the output: RepeatMasker will generate several output files, including a .out file. You can parse this file to extract the information you need. There is a Perl tool called "one_code_to_find_them_all.pl" that can help you parse RepeatMasker output files[0]. You can download it from the source provided.
  1. Use the provided Perl script: Once you have the "one_code_to_find_them_all.pl" script, you can run it to conveniently parse the RepeatMasker output files. Here's an example of how to use it:

perl one_code_to_find_them_all.pl --rm <RepeatMasker_out_file> --length <length_file>

 

Replace <RepeatMasker_out_file> with the path to your RepeatMasker .out file, and <length_file> with the path to a file containing the lengths of the reference elements.

This script will generate several output files, including .log.txt and .copynumber.csv, which contain quantitative information about the identified repeat elements.

Remember to adjust the parameters and options according to your specific needs and the characteristics of your genome.

]]> Neel 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/news/view/1467/biopython-cookbook Thu, 08 Aug 2013 06:43:02 -0500 https://bioinformaticsonline.com/news/view/1467/biopython-cookbook <![CDATA[BioPython Cookbook]]> If you are planning to start learning BioPython ( it does not bite but swallow :P just kidding) then this online cookbook will be really helpful for you.

http://biopython.org/DIST/docs/tutorial/Tutorial.html

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/17926/orange-bioinformatics-2534 Mon, 06 Oct 2014 12:51:37 -0500 https://bioinformaticsonline.com/bookmarks/view/17926/orange-bioinformatics-2534 <![CDATA[Orange-Bioinformatics 2.5.34]]> Orange Bioinformatics extends Orange, a data mining software package, with common functionality for bioinformatics. The provided functionality can be accessed as a Python library or through a visual programming interface (Orange Canvas). The latter is also suitable for non-programmers.

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Address of the bookmark: https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34

]]> Robert M Willioms https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn Mon, 29 Feb 2016 17:39:24 -0600 https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn <![CDATA[scikit-learn]]> Machine Learning in Python

Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license

More at http://scikit-learn.org/stable/index.html

 

Address of the bookmark: http://scikit-learn.org/stable/auto_examples/index.html

]]> Jitendra Prajapati https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40221/dash-a-web-application-framework-that-provides-pure-python-abstraction-around-html-css-and-javascript Tue, 05 Nov 2019 06:39:48 -0600 https://bioinformaticsonline.com/bookmarks/view/40221/dash-a-web-application-framework-that-provides-pure-python-abstraction-around-html-css-and-javascript <![CDATA[Dash: a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.]]> Dash is a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.

Dash Bio is a suite of bioinformatics components that make it simpler to analyze and visualize bioinformatics data and interact with them in a Dash application.

The source can be found on GitHub at plotly/dash-bio.

These docs are using Dash Bio version 0.1.4.

Address of the bookmark: https://dash.plot.ly/dash-bio

]]> Jit