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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
Published on: June 23, 2012
PineSAP--sequence alignment and SNP identification pipeline
Jill L Wegrzyn1, Jennifer M Lee, John Liechty
1Department of Plant Sciences, University of California, Davis, CA 95616, USA. jlwegrzyn@ucdavis.edu
Bioinformatics (Oxford, England)
|August 12, 2009
Summary
The Pine Alignment and SNP Identification Pipeline (PineSAP) enhances single nucleotide polymorphism (SNP) prediction. This high-throughput tool uses machine learning for faster, more accurate SNP identification in eukaryotic species.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Accurate single nucleotide polymorphism (SNP) identification is crucial for genomic studies.
- Existing re-sequencing data analysis pipelines often face limitations in speed and accuracy.
Purpose of the Study:
- To develop a high-throughput solution for SNP prediction using multiple sequence alignments.
- To improve the speed and accuracy of SNP calling from re-sequencing data.
- To create a versatile pipeline applicable to eukaryotic species lacking complete genome sequences.
Main Methods:
- Integration of customized scripting and existing bioinformatics utilities.
- Implementation of a hybrid approach combining traditional methods with machine learning algorithms.
- Utilizing multiple sequence alignments derived from re-sequencing data for SNP prediction.
Main Results:
- PineSAP significantly improves the quality of multiple sequence alignments.
- The pipeline demonstrates enhanced accuracy in identifying single nucleotide polymorphisms (SNPs) compared to existing solutions.
- Machine learning integration allows for broader applicability across diverse eukaryotic species.
Conclusions:
- The Pine Alignment and SNP Identification Pipeline (PineSAP) offers a robust and efficient method for SNP discovery.
- PineSAP's machine learning component enhances its utility for non-model organisms.
- The pipeline represents a significant advancement in high-throughput SNP identification from re-sequencing data.
Related Concept Videos
01:05Single Nucleotide Polymorphisms-SNPs
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,...
02:26Comparing Copy Number Variations and SNPs
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%...
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%...
03:21RNA-seq
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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