Titlebook: Deep Sequencing Data Analysis; Noam Shomron Book 2021Latest edition Springer Science+Business Media, LLC, part of Springer Nature 2021 ChI

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Detecting Causal Variants in Mendelian Disorders Using Whole-Genome Sequencing,tect each variant class and describe strategies to increase the likelihood of detecting causal variant(s) in Mendelian disease. Lastly, we describe a sample workflow for synthesizing large amount of genetic information into concise clinical reports.

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Interrogating the Accessible Chromatin Landscape of Eukaryote Genomes Using ATAC-seq,s of common such analytical procedures (including data processing, quality control, peak calling, identifying differentially accessible open chromatin regions, and variable transcription factor (TF) motif accessibility) and discuss recommended optimal practices.

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Book 2021Latest editionncing data field. Chapters guide readers through techniques for processing RNA-seq data, microbiome analysis, deep learning methodologies, and various approaches for the identification of sequence variants. Written in the highly successful .Methods in Molecular Biology .series format, chapters inclu

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Microbiome Analysis Using 16S Amplicon Sequencing: From Samples to ASVs,re not detailed, but rather the impact of various experimental decisions on the downstream analysis is described. Emphasis is made on the transition from reads to ASVs, describing the Deblur algorithm.

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Investigating Specialist Discoursee the efficiency of many high-throughput sequencing data analyses. But, generating minimum-size universal hitting sets is a hard problem. In this chapter, we cover our algorithmic developments to produce compact universal hitting sets and some of their potential applications.

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Lina Landinez,Thorsten Kliewe,Habtamu Diribaent challenges of single-cell data analysis and the computational methods used to overcome them. We cover current and future applications of scRNAseq in research of cellular dynamics and as an integrative component of biological research.

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Improved Analysis of High-Throughput Sequencing Data Using Small Universal ,-Mer Hitting Sets,e the efficiency of many high-throughput sequencing data analyses. But, generating minimum-size universal hitting sets is a hard problem. In this chapter, we cover our algorithmic developments to produce compact universal hitting sets and some of their potential applications.