BIOINFORMATIX LEARNING PATH

Become an NGS Analyst

Learn how sequencing data moves from raw FASTQ files through quality control, alignment and downstream genomic analysis.

FASTQ→QC→ALIGNMENT→ANALYSIS→INTERPRETATION
sequencing_run.fastq
ACGTTGCACGTTGCATGTTGCATCTTGCATCGTGCATCGA
REFERENCEACGTTGCATCGATGCA

BEFORE YOU BEGIN

Recommended Foundations

You do not need advanced programming, but these basics will make the NGS analyst path more productive.

  • Biology fundamentals
  • Linux
  • Basic statistics
  • Basic Python or R
  • Biological databases
Build the foundations in the Beginner Path

FOLLOW THE SEQUENCING DATA

From Biological Sample to Downstream Analysis

Every file in an NGS workflow represents a transformation. Follow the data and understand why each step exists before memorizing software.

  1. 01Biological SampleTissue, cells or community
  2. 02DNA / RNAExtracted molecules
  3. 03Library PreparationFragments plus adapters
  4. 04SequencingInstrument signals
  5. 05FASTQReads plus quality
  6. 06Quality ControlInspect data quality
  7. 07PreprocessingClean only when justified
  8. 08Alignment / AssemblyMap or reconstruct
  9. 09SAM / BAMAlignment records
  10. 10Downstream AnalysisAnswer the question

CORE NGS COMPETENCIES

Learn the Pipeline, One Data State at a Time

These six stages build the technical foundation required before choosing a specialized NGS analysis branch.

01
FOUNDATIONS

Understand How Sequencing Produces Computational Data

NGS instruments measure millions of DNA or RNA fragments in parallel. Learn how experimental choices shape the reads you receive.

Reads

Short sequence observations generated by the instrument.

Depth & Coverage

How much data was generated and how often positions are observed.

Paired vs Single

One end or both ends of each fragment are sequenced.

Reference vs De Novo

Map to an existing genome or reconstruct without one.

Quality Scores

Estimated confidence for each called base.

Sequencing Errors

Technical uncertainty that can affect conclusions.

✓ Milestone: I understand how sequencing produces computational data.
02
RAW DATA

Read a FASTQ Record

FASTQ stores the called sequence and an encoded quality score for every base. It is the usual starting point for NGS analysis.

@SEQ_ID          1  Identifier
GATTTGGGGTTCAAAG   2  Sequence
+                3  Separator
!''*((((***+))%%   4  Quality

Phred Quality, Conceptually

Higher characters encode greater confidence in a base call. Quality is probabilistic, not a guarantee.

LOWHIGH
FASTASequence only>read
GATT...
FASTQSequence + quality@read
GATT...
+
IIII...
✓ Milestone: I can inspect and understand raw sequencing reads.
03
DIAGNOSE FIRST

Assess Quality Before Changing the Data

FastQC summarizes individual files. MultiQC conceptually brings reports together so patterns across samples are easier to see.

Per-base quality
  • PASS Sequence quality
  • CHECK GC content
  • CHECK Adapter contamination
  • PASS Read length
  • REVIEW Duplication
FastQCMultiQC conceptGC contentDuplicationRead length
04
DECISION POINT

Preprocess Only When the Evidence Supports It

Adapter trimming, quality filtering and read cleaning can improve some analyses, but unnecessary processing can remove useful data or introduce bias.

QC EVIDENCE
Adapter contamination?Consider adapter trimming
Poor terminal quality?Evaluate quality filtering
No clear issue?Preserve the reads
05
REFERENCE-BASED ANALYSIS

Understand Read Alignment

An aligner compares reads with an indexed reference genome and reports where each read maps, if a suitable match can be found.

REFERENCE...ACGTTGCATCGATGCA...
READS  ACGTTGCA
    TTGCATCG
      GCATCGAT
MappedUnmappedMapping quality
BWACommon for DNA reads
Bowtie2General short-read alignment
HISAT2Splice-aware RNA alignment
STARFast splice-aware RNA alignment
06
ALIGNMENT DATA

Work With SAM and BAM

SAM is a text representation of alignments. BAM stores the same type of information efficiently in binary form. SAMtools supports common inspection and processing operations.

  1. FASTQ
  2. ALIGNER
  3. SAM
  4. BAM
  5. SORT
  6. INDEX
Alignment Records

Where and how each read maps.

Flags

Compact properties of a read and its pair.

Mapping Quality

Confidence in the reported placement.

Sorting

Orders records, commonly by coordinate.

Indexing

Enables rapid access to genomic regions.

STAGE 07: DOWNSTREAM ANALYSIS

One FASTQ Starting Point, Five Analysis Branches

Select a branch to inspect its goal, input, processing stages and final output.

FASTQRAW READS
GOAL

Identify sequence differences relative to a reference.

INPUT

DNA sequencing FASTQ files

  1. BAM
  2. Variant Calling
  3. VCF
  4. Annotation
FINAL OUTPUT

Annotated and filtered variants

FILE FORMAT SYSTEM

Know What Each File Represents

File formats are checkpoints in the analysis. Recognizing them helps you trace inputs, transformations and outputs.

FASTQRaw Reads
SAM/BAMAlignments
VCFVariants
GTF/GFFAnnotations
BEDGenomic Regions
FASTAReference Sequences
COUNT MATRIXExpression

NGS TOOLBOX

Choose Tools by Function

This is a working vocabulary, not a requirement to master every program.

QC

Quality Control

  • FastQC
  • MultiQC concept
MAP

Alignment

  • BWA
  • Bowtie2
  • HISAT2
  • STAR
FILE

File Manipulation

  • SAMtools
  • BEDTools
VAR

Variant Analysis

  • bcftools
  • GATK concepts
RNA

RNA-Seq

  • featureCounts
  • DESeq2
ASM

Assembly

  • SPAdes
  • Assembly concepts

PROJECT PROGRESSION

Build an NGS Analysis Portfolio

Progress from inspecting one file to designing and documenting an independent analysis.

  1. 01
    PROJECT 1

    FASTQ Quality Assessment

    Inspect reads, explain QC patterns and recommend justified actions.

    Output: concise QC report
  2. 02
    PROJECT 2

    Read Alignment and BAM Processing

    Map reads, inspect alignments, sort and index a BAM file.

    Output: processed BAM plus workflow notes
  3. 03
    PROJECT 3

    Small Variant Calling Workflow

    Follow a basic reference-to-VCF analysis and interpret filtered calls.

    Output: filtered VCF and limitations
  4. 04
    PROJECT 4

    RNA-Seq Workflow

    Move from FASTQ through counts to differential expression.

    Output: results table, figures and interpretation
  5. 05
    PROJECT 5

    Independent NGS Dataset Analysis

    Choose a public dataset, frame a question and document a reproducible end-to-end workflow.

    Output: portfolio-ready repository and report

LEARN WITH BIOINFORMATIX

Courses Connected to Each Branch

Use structured training to support a workflow you are ready to practice.

VARIANT CALLINGLearn Variant Calling: NGS Data AnalysisView course ↗
RNA-SEQHands-on RNA-Seq Analysis: From FASTQ to DEGsView course ↗
ASSEMBLYLearn Genome Assembly and Annotation in Prokaryotes and EukaryotesView course ↗
METAGENOMICSMaster Metagenomics & Microbiome Data AnalysisView course ↗
EPIGENOMICSLearn ChIP-Seq & scATAC-Seq Data AnalysisView course ↗
MICROBIAL NGSLearn Antimicrobial Resistance Detection & AnalysisView course ↗

SELF-ASSESSMENT

NGS Readiness Check

Mark the skills you can explain or perform without step-by-step guidance. Progress is saved only in this browser.

0% complete
NGS readiness skills

CONTINUE LEARNING

Choose Your Next Path

01

RNA-Seq Analyst Path

Build depth in expression analysis and transcriptomics.

Explore path →
02

Bioinformatics Analyst Path

Strengthen broader computational and reproducibility skills.

Explore path →
03

AI + Bioinformatics Path

Apply machine learning to biological data after the foundations are strong.

Explore path →

FOLLOW THE DATA

Turn Raw Sequencing Reads Into Biological Insight

Understand each file, justify each decision and document every transformation from FASTQ to interpretation.

Explore NGS CoursesStart an NGS ProjectChoose a Specialization

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