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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
18.3K
Chromatin Position Affects Gene Expression02:35

Chromatin Position Affects Gene Expression

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
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Position-effect Variegation02:32

Position-effect Variegation

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In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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Related Experiment Video

Updated: Jan 28, 2026

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

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SNPs detection by eBWT positional clustering.

Nicola Prezza1, Nadia Pisanti1,2, Marinella Sciortino3

  • 11Dipartimento di Informatica, University of Pisa, Pisa, Italy.

Algorithms for Molecular Biology : AMB
|March 7, 2019
PubMed
Summary

We introduce positional clustering theory to analyze sequencing data. This alignment-free method efficiently identifies single nucleotide polymorphisms (SNPs) directly from raw reads, offering a promising approach for variant calling.

Keywords:
Assembly-freeBWTLCP arrayReference-freeSNPs

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Rapid advancements in sequencing technology necessitate efficient data structures for storing and analyzing raw sequencing reads.
  • There is a growing demand for alignment-free and reference-free variant calling methods that operate directly on indexed raw reads.
  • Current methods often rely on reference genomes, limiting their applicability in certain research scenarios.

Purpose of the Study:

  • To develop a novel theoretical framework, positional clustering, for analyzing sequencing data.
  • To design and implement an alignment-free and reference-free method for single nucleotide polymorphism (SNP) calling.
  • To evaluate the efficacy of the proposed method on both synthetic and real sequencing data.

Main Methods:

  • Development of the positional clustering theory based on the extended Burrows-Wheeler Transform (eBWT) and LCP array.
  • Design and implementation of an alignment-free and reference-free SNP calling pipeline utilizing the eBWT and LCP arrays.
  • Experimental validation using synthetic datasets and real sequencing data to assess performance and accuracy.

Main Results:

  • The positional clustering theory accurately describes how bases covering the same genome position cluster in the eBWT.
  • A simple scan of the eBWT and LCP arrays allows for the detection of SNPs within these clusters.
  • The implemented tool provides an intrinsic reference-free evaluation of accuracy by reporting SNP coverage.

Conclusions:

  • The positional clustering framework is effective for identifying SNPs directly from raw sequencing data.
  • This approach offers a promising avenue for calling other types of genetic variants without relying on a reference genome.
  • The developed software, ebwt2snp, is freely available for academic use, facilitating further research in this area.