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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Related Experiment Video

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Fast, Ungapped Reads Mapping Using Squid.

Christopher Riccardi1, Gabriel Innocenti1,2, Marco Fondi1

  • 1Department of Biology, University of Florence, Via Madonna del Piano 6, 50019 Sesto F.no, Florence, Italy.

International Journal of Environmental Research and Public Health
|May 14, 2022
PubMed
Summary
This summary is machine-generated.

Next Generation Sequencing (NGS) generates vast data, but contaminants can skew results. A new tool, Squid, identifies unmapped or discordant sequencing reads, improving data accuracy for omics studies.

Keywords:
dynamic programmingmappingquality checkrna-seq

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Next Generation Sequencing (NGS) technologies offer unprecedented genomic detail for omics studies.
  • NGS applications include population disease analysis and microbial community impact on human health.
  • While NGS protocols minimize contamination, a fraction of reads may not represent the intended target.

Purpose of the Study:

  • To introduce Squid, a novel bioinformatics tool for analyzing sequencing reads.
  • To identify and extract sequencing reads that do not map to a reference genome.
  • To provide a pre-quantification step for assessing data quality and potential contaminants.

Main Methods:

  • Development of a new sequence mapper named Squid.
  • Squid identifies reads matching or not matching a reference sequence database in any orientation.
  • The tool is designed to handle unmapped and discordantly mapped reads.

Main Results:

  • Squid effectively finds and returns sequencing reads that do not map to a reference.
  • The tool can identify reads matching in any orientation.
  • This functionality aids in assessing the presence of contaminants.

Conclusions:

  • Squid serves as a valuable pre-processing tool for omics pipelines, particularly RNA-Seq.
  • It enhances data integrity by identifying non-target reads.
  • Utilizing Squid prior to quantification can improve the reliability of omics study findings.