Bioinformatics: Learn NGS Data Analysis From Scratch

Explore the fundamental structure of DNA, including nucleotides, phosphate, and deoxyribose, and the four bases: adenine, thymine, cytosine, guanine, forming a double-stranded helix.

Discover where DNA resides in cells by comparing eukaryotic nuclei and prokaryotic cytoplasm, and learn how DNA threads organize into chromosomes wrapped around histone scaffolds.

Explore why dna is essential for life by tracing fertilization to the zygote, nucleus dna, and 23 chromosomes from each parent, linking genotype to phenotype.

Explore how genotype shapes phenotype by tracing how coding DNA genes produce proteins through transcription and translation—the central dogma—while noting the roles of non-coding DNA.

Explore how genes encode protein information through open reading frames and codons, including stop codons and degeneracy, with exons, introns, promoters, termination sequences, and enhancers regulating transcription.

Compare prokaryotic and eukaryotic architecture: prokaryotes have tandemly arranged, intronless genes; eukaryotes have dispersed genes with introns and large non-coding DNA. Start sites differ: Shine-Dalgarno in prokaryotes, Kozak in eukaryotes.

Explore how mutations arise in DNA, drive evolution, and influence genotype and phenotype, with examples like sickle cell and malaria, and learn how next generation sequencing reveals these variants.

Explore single nucleotide variations and large chromosomal mutations, including substitutions, insertions, and deletions. Learn how transitions and transversions relate to silent, missense, nonsense, and frameshift mutations.

Explore large chromosomal changes, including deletions, duplications, translocations, and CNVs, and understand how these variations shape gene expression and phenotypes.

Differentiate germline mutations inherited at birth from somatic mutations acquired after birth due to environmental factors, and explore their distinct implications for cancer and NGS data analysis.

Learn that SNPs are mutations with a frequency of at least 1% in populations. NGS studies identify these SNPs to inform population-specific therapeutics, reflecting individual genetic differences.

Describe the Sanger sequencing method, using dideoxy dNTPs to terminate DNA synthesis and generate labeled fragments. Explain how chromatograms reveal the sequence and the method's single target limitation.

Explore Sanger sequencing quality using FASTA format, headers, and chromatogram peaks, where non-overlapping colors signal reliable data. Note major limitations: slow throughput and 700–2000 bp reads, guiding to next-generation sequencing.

Analyze Sanger sequencing quality by viewing the faster sequence data file and the .ab1 quality file, and use Eugene to inspect chromatograms for end overlaps.

Explore the NGS wet-lab workflow from DNA extraction and restriction enzyme digestion to adapter ligation, library preparation, and parallel sequencing on an NGS chip for FASTQ data used in analysis.

Explore the FASTQ file format used in next-generation sequencing, detailing four-line records with a header starting at '@', sequences (A, T, C, G, N), a plus sign, and quality scores.

Learn how Phred quality scores in fastq files are represented by symbols, numbers, and letters from 0 to 40, and how to interpret them with specialized analysis tools.

Understand paired end and single end sequencing by examining forward and reverse DNA strands, and learn how reads in fastq reflect these data types.

Learn the end-to-end ngs data analysis workflow: quality check with fastqc, trim with trimmomatic, align with bwa-mem, call variants with Genome Analysis Toolkit, and annotate vcf for SNPs and mutations.

Explore the fundamentals of computer hardware and operating systems, revealing how RAM, CPUs, and hard drives support NGS data analysis on Linux, with open source, security, and performance advantages.

Discover how to install the Linux operating system for ngs data analysis, including options for Windows users and guidance on using Mac terminals.

Install a Linux environment in Windows using Windows Subsystem for Linux and Ubuntu from the Microsoft Store, enabling use of Linux bioinformatics tools like Gromacs on Windows.

Apply essential linux navigation using cd to change directories and ls to list contents. Understand file paths, root directory, and commands like cd .. and cd to move between drives.

Learn to install essential Linux tools for ngs data analysis, including fastqc, trimmomatic, samtools, bwa, and gatk, using sudo apt install and related commands.

Update and upgrade your Linux system, install fast QC, Bawa, and the Sam tools with sudo apt, verify installations, and stay tuned for Trimmomatic in the next video.

Install hisat2 with sudo apt install and verify by running hisat2 for NGS data analysis.

Download and install Trimmomatic, learn to trim low-quality reads and adapters, and set up Java in Linux to run the executable jar file from the downloaded package.

download and set up GATK, the genome analysis toolkit, from GitHub; extract the package, add GATK to your Linux path via bashrc, source, and verify by running GATK.

Learn to perform quality checks on gzipped FASTQ files with FASTQC in Ubuntu, analyzing paired forward and reverse reads and interpreting the resulting HTML quality reports.

Evaluate fastqc quality reports from HTML outputs to assess fastq file quality, including phred scores, per-base sequence quality, GC content, adapters, and trimming needs.

Trim poor quality reads from a fastq file with Trimmomatic in Ubuntu, using Java -jar, phred 33 encoding, and a trailing threshold of 10 for single-end reads.

Learn the fundamentals of alignment as the backbone of the NGS data analysis pipeline, matching reads to a reference genome and addressing duplicates and gaps.

Explore common ngs aligners—Bawa, Bowtie Star, and Hisat2—and compare memory needs, speed, and Gatc pipeline suitability, with guidance on using Hisat2 and Bawa.

Increase swap memory in Ubuntu to handle memory shortages during the NGS alignment process. The lecture guides commands to create, set permissions, and activate the swap file.

Explore sam and bam files after sequence alignment; contrast sam (human-readable) with bam (binary), and explain the sam structure, header and alignment regions, plus the flag and cigar columns.

Learn how to download the human reference genome for the Hisat2 aligner, selecting hg38/grch38 from the official page, decompressing the file, and using genome indices for alignment.

Download the Solomon fish reference genome from the Ensembl genome browser, read the readme to pick the right fasta, and build Hisat2 compatible indices on Ubuntu.

Identify the read group and include it in the same file for the GATK pipeline, using the header line to capture sample id, flow cell, lane, and r1/r2 status.

Learn to retrieve read group information from a compressed fastq file using a single ubuntu command with zcat and head, enabling its use in hisat2 workflows.

Align two paired-end fastq files with hisat2 using a reference genome, adding read group information (--rg) to produce aligned.sam for a Gatk NGS data analysis pipeline.

Download the reference genome—the backbone of the alignment—for the NGS data analysis pipeline. Obtain a single fasta file for bwa; Hg38 is available from Ensembl or UCSC.

Create index files for the hg38 reference genome with Baba to enable fastq alignment, noting a ~5 GB index and CPU impact, plus an option to download via Google Drive.

Learn how to align reads to the hg38 reference with bwa mem on a high-resource workstation, including setting read groups with -R and producing out.sam; note hisat2 for lower-resource setups.

Convert the sam file to bam with samtools view using CPUs to create aligned.bam, then sort to produce a final sorted bam for gatk downstream analysis.

Learn to mark duplicates in bam files, understand how PCR and optical duplicates inflate coverage and affect mutation frequency, and apply marking tools.

Mark duplicates in the final_sorted.bam file with samtools, generating mod_duplicate.bam; note processing is quick for small files but may take longer for large bam files before the Gatk pipeline begins.

Recalibrate base quality scores to correct machine biases using a two-stage BQSR model built from known sites VCF, excluding known variants and applying to unknowns.

Learn to generate the fasta index and dictionary files with samtools faidx and GATK CreateSequenceDictionary for a reference genome, enabling base recalibration in a GATK pipeline.

Perform base recalibration (BQSR) on the mock_duplicate.bam with SG 38 dot f and the dbSNP 138.vcf to produce recall_data.table, then apply BQSR to mark_duplicate.bam using hg38.fa.

Learn how the GATK haplotype caller identifies variants by scanning the bam file against a reference genome, builds and tests haplotypes, and reports real changes in a vcf file.

Apply base recalibration to the bam file, then use GATK HaplotypeCaller to convert the sorted bam into a vcf, producing udemy_test.vcf.

Open and inspect VCF files in text editors and Excel, using tab-delimited data to view chromosome, position, reference and alternative alleles, quality filters, and info formats.

Annotate the bam and vcf inputs using a variant annotator with the reference genome and dbsnp known sites to produce an annotated vcf containing rsids.

Learn to separate snps and indels in a vcf file using the Get Select variant module. Specify the reference genome and input vcf to output snp_Udemy.vcf and indel_Udemy.vcf.

Explore the structure of the VCF file, including the metadata and the eight mandatory variant-table columns plus two sample columns, with notes on AC, AF, and DP.

Explain how the vcf format encodes genotype information with gt, ad, dp, gq, and pl, and how sample values map to reference and alternative alleles, including phased and unphased genotypes.

Learn how allelic depth (AD) and total depth (DP) quantify reads at a genomic position, including reference reads and two variant reads, and how DP sums the allelic depth reads.

Learn how genotype quality (GQ) and genotype likelihood (PL) guide NGS variant calls, with high GQ indicating reliability and the smallest PL determining the final genotype, including clinical implications.

Sort variants in a VCF file in Excel by using text to column to split sample values by colon, then filter by genotype quality >50 to prioritize clinically important variants.

Discover Funcotator, a gateway pipeline tool that annotates VCF variants with gene context, functional impact, population frequencies, clinical relevance, and conservation scores using germline and somatic data sources.

Download germline variant data sources for annotation using get k and define the HG38 reference genome. Explore the process of downloading, extracting, and organizing data folders for downstream variant analysis.

Annotate vcf files with funcotator using the hg38 reference and germline data sources, producing a fun_annotated.vcf with canonical transcript annotations.

Inspect the annotated VCF to identify disease-associated variants using Funcotator's annotations. Open the VCF in Excel, expand the info column, and examine fields like Gencode_43 and Hugo_symbol.

Sort VCF files into a human-readable TSV, then examine the annotated genome add output showing allele frequencies of variants across populations, and view the parsed CSV in Excel.

Learn to sort an annotated vcf file into a tidy tsv with a Python script on Ubuntu, producing a human readable, tab separated file for easy sorting in Excel.

Demonstrates annotating a vcf file with somatic data sources, converting to tsv for easier analysis in Excel, and exploring annotations such as gene symbols, Ensembl IDs, and cosmic tissue type.

Learn to filter variants from NGS data using a Python script in Ubuntu, producing a tissue-associated breast cancer variant set from an annotated semantic parse CSV.