Special Issue "Pipeline Tools for Next Generation Sequencing Analysis"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editor

Dr. Thomas Carroll
E-Mail Website
Guest Editor
Bioinformatics Resource Centre, The Rockefeller University, New York, 10065 NY, USA
Interests: Bioinformatics; chip-seq; RNA-seq

Special Issue Information

Dear Colleagues,

The use of Next Generation Sequencing (NGS) technologies in the interrogation of hypotheses has become increasing common in medical and biological research. With the advent of multiplexing technologies, the increase in sequence output from NGS machines and the development of novel applications for NGS, the complexity of high-throughput sequencing experiments has increased coordinately. The analysis of data from this diverse range of NGS applications requires a complex interaction between generic NGS data processing and application specific software tools while maintaining the high levels of reproducibility required in research. The use of pipelines and workflows in the analysis of NGS applications offers not only a high-throughput, automated processing and analysis of the data but a mechanism to enhance reproducibility and establish best practices in the analysis of NGS application types.

In this Special Issue, the contributing authors will present the most recent developments in pipelines and workflows for the analysis of both established and emerging NGS sequencing applications and technologies.

Dr. Thomas Carroll
Guest Editor

Manuscript Submission Information

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Keywords

  • Next Generation Sequencing
  • Bioinformatics
  • Workflow
  • Pipeline
  • RNA-seq
  • RNAseq
  • ChIP-seq
  • ChIPseq
  • ATAC-seq
  • ATACseq
  • WSG
  • Transcriptomics
  • Epigenetics
  • Analysis

Published Papers (1 paper)

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Research

Open AccessArticle
SiNPle: Fast and Sensitive Variant Calling for Deep Sequencing Data
Genes 2019, 10(8), 561; https://doi.org/10.3390/genes10080561 - 25 Jul 2019
Abstract
Current high-throughput sequencing technologies can generate sequence data and provide information on the genetic composition of samples at very high coverage. Deep sequencing approaches enable the detection of rare variants in heterogeneous samples, such as viral quasi-species, but also have the undesired effect [...] Read more.
Current high-throughput sequencing technologies can generate sequence data and provide information on the genetic composition of samples at very high coverage. Deep sequencing approaches enable the detection of rare variants in heterogeneous samples, such as viral quasi-species, but also have the undesired effect of amplifying sequencing errors and artefacts. Distinguishing real variants from such noise is not straightforward. Variant callers that can handle pooled samples can be in trouble at extremely high read depths, while at lower depths sensitivity is often sacrificed to specificity. In this paper, we propose SiNPle (Simplified Inference of Novel Polymorphisms from Large coveragE), a fast and effective software for variant calling. SiNPle is based on a simplified Bayesian approach to compute the posterior probability that a variant is not generated by sequencing errors or PCR artefacts. The Bayesian model takes into consideration individual base qualities as well as their distribution, the baseline error rates during both the sequencing and the PCR stage, the prior distribution of variant frequencies and their strandedness. Our approach leads to an approximate but extremely fast computation of posterior probabilities even for very high coverage data, since the expression for the posterior distribution is a simple analytical formula in terms of summary statistics for the variants appearing at each site in the genome. These statistics can be used to filter out putative SNPs and indels according to the required level of sensitivity. We tested SiNPle on several simulated and real-life viral datasets to show that it is faster and more sensitive than existing methods. The source code for SiNPle is freely available to download and compile, or as a Conda/Bioconda package. Full article
(This article belongs to the Special Issue Pipeline Tools for Next Generation Sequencing Analysis)
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