Genomic Selection Signature Pipeline using Hp Statistics

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[b]Genomic Selection Signature Pipeline using Hp Statistics[/b]
Published 07/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 12 lectures (4h 28m) | Size: 2.82 GB
Whole-Genome Pooled-Seq Data Analysis using Hp Method

What you'll learn
Genomic selection signature bioinformatics pipeline
Linux bash scripting for bioinformatics
Pooled Heterozygosity statistical methodology
R integration in bioinformatics data analysis

Requirements
Basics of Linux and R
Basics of NGS data

Description
In this course, audience will learn complete bioinformatics pipeline for detecting genomic selection signatures using pooled-seq whole-genome sequencing data with the help of Linux & R. Basics bash command-line and R scripting is being used for running the different steps involved in this pipeline. As there are many statistical methods for detecting positive selective sweeps in the the genome which entirely depends on the type of omics data generated. we used pooled-heterozygosity statistics (Hp) method which is robust in case of pooled-seq dataset with sliding window approach of 150kb.

Trimming of the sequences was performed with Trimmomatic software followed by indexing of the reference genome and mapping with BWA-MEM, sorting and mark duplication steps were performed with Picard tool, then SAMtools was used for making the pileup files, further, PoPoolation2 tools was used to generate the .rc files and to synchronize the files. Finally, in-housed Ruby Hp script was used to find the hitchhiking positive selection pressure in the subject genome. A brief commentary was also given on Hp adopted statistics, moreover, data was prepared and normalized to visualize it on R. Manhattan, qq density plots and histograms were generated and finally Tajima's D statistics was also applied for analyzing the same data using this classical method of site frequency spectrum.

Who this course is for
Beginners in omics data science, bioinformatics, molecular genetics and genomics

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