DNA Data File Formats: Understanding Your Raw Genetic Data

A beginner-friendly guide to FASTQ, FASTA, FNA, FAA, GTF, GFF, BED, SAM, BAM, CRAM, VCF, BCF, CSV, TSV, MTX, HDF5, H5AD, GenBank, EMBL, PDB, PDBx/mmCIF, Newick, and compressed files—and how they fit into a typical NGS workflow.

Introduction

When you receive your genetic data, you may see file names such as 23andMe, AncestryDNA, FASTQ, BAM, VCF, or IDAT. But what do these files actually mean?

If you are a consumer, you probably do not need to understand every technical bioinformatics format. The most important thing is to know what type of genetic information your file contains and how it can be used. This guide explains the common DNA data formats in simple language and shows which formats you may receive from different types of genetic testing. Bioinformatics is a field that uses computers to store, process, and understand biological data. One of the most common types of biological data is DNA sequencing data. DNA sequencing means finding the exact order of the four basic building blocks, or bases, present in DNA: A – Adenine, T – Thymine, G – Guanine, and C – Cytosine.

When a DNA sequencing experiment is performed, the result is not usually one simple file. The data passes through several steps, and each step may use a different file format.

A file format is a specific way of organizing information inside a computer file.

During a typical sequencing analysis, you may see files such as:

FASTQ → FASTA → SAM/BAM/CRAM → VCF → TSV/CSV → Final Report

For someone new to bioinformatics, these names can look confusing. What does FASTQ mean? What is the difference between SAM and BAM? Why do we need a VCF file? What is GTF used for?

This blog explains the most commonly used bioinformatics file formats in simple language and shows how they fit into a typical sequencing workflow.

Consumer-Friendly Raw DNA Formats

·  23andMe format

·  AncestryDNA format

·  These are the formats common users recognize.

·  They are generally human-readable text files containing genotype information.

·  Compatible third-party platforms can use these files for additional analysis.

23andMe-style raw data is a commonly recognized consumer genetic-data format. It contains genotype information for selected genetic markers and can be used by compatible third-party genetic-analysis services. Because these consumer raw-data files follow recognizable structures, compatible third-party websites can process them. For example, users may upload compatible raw DNA data to services such as GEDmatch or IllustrativeDNA for additional ancestry and genetic analysis.

AncestryDNA raw data is another consumer-facing genotype-data format. Like 23andMe-style data, it is designed to represent genetic variants in a relatively simple, human-readable text format.

Important: Consumer raw-data formats such as 23andMe-style files are not the same as the complete raw sequencing data generated by Whole Genome Sequencing (WGS).

1. What Is a Bioinformatics File Format?

A bioinformatics file format is a particular way of storing biological information so that computers and bioinformatics software can read and process it.

A file may contain DNA sequences, RNA sequences, protein sequences, sequencing reads, quality scores, genes, genome regions, sequence alignments, genetic variants, gene expression, protein structures, or evolutionary relationships.

Different types of information require different file formats.

FASTQ stores sequencing reads and their quality information. FASTA stores DNA, RNA, or protein sequences. SAM, BAM, and CRAM store information about how sequencing reads match a reference genome. VCF stores genetic variants. GTF and GFF store information about genes and other features present in a genome.

Therefore, understanding file formats is one of the first useful skills for anyone learning bioinformatics.

2. FASTQ – Sequencing Reads and Quality Information

FASTQ is one of the most important file formats in Next-Generation Sequencing (NGS).

Next-Generation Sequencing (NGS) refers to modern sequencing technologies that can read millions or billions of DNA or RNA fragments in a single experiment.

A sequencing machine produces many short DNA or RNA sequences. These short sequences are called reads.

A read is a sequence of bases obtained from a sequencing machine.

A FASTQ file stores two important pieces of information: the sequence of each read and the quality of each base in the read.

A simple FASTQ record looks like:

@Read_001
ACGTACGTACGT
+
IIIIIIIIIIII

A FASTQ record normally contains four lines.

Line 1 – Read identifier: This line gives the read a name or identification number.

Line 2 – Sequence: This is the DNA sequence obtained from the sequencing machine.

Line 3 – Separator: The + symbol separates the DNA sequence from its quality information.

Line 4 – Quality information: This represents the quality of each base. A quality score tells us how confident the sequencing machine was that a particular base was read correctly. A higher quality score generally means greater confidence in the base call.

FASTQ is often the starting point for an NGS analysis.

FASTQ → Quality Control → Trimming → Alignment → Variant Calling

FASTQ files can be very large because a sequencing experiment may produce millions or billions of reads.

3. FASTA – Biological Sequences

FASTA is another very common format used in bioinformatics.

A FASTA file stores biological sequences such as DNA sequences, RNA sequences, and protein sequences.

Unlike FASTQ, FASTA normally does not contain sequencing quality scores.

A simple FASTA record looks like:

>Human_gene
ATGCGTACGTAGCTAGCTAG

The first line starts with > and contains the name or identification of the sequence. The next line contains the actual sequence.

FASTA vs FASTQ:

FASTA = sequence
FASTQ = sequence + quality information

FASTA files are commonly used for reference genomes, gene sequences, transcript sequences, protein sequences, and genome assemblies.

4. FNA – Nucleotide FASTA

FNA usually refers to a FASTA file containing nucleotide sequences.

A nucleotide is one of the basic units that make up DNA or RNA.

DNA contains A – Adenine, T – Thymine, G – Guanine, and C – Cytosine. RNA generally uses U – Uracil instead of T.

An FNA file may contain whole genome sequences, chromosome sequences, DNA fragments, contigs, or scaffolds.

A contig is a continuous DNA sequence assembled from overlapping sequencing reads. A scaffold is a larger reconstructed genomic region made by connecting contigs, sometimes with gaps between them.

In simple terms: FNA = nucleotide sequences stored in FASTA format.

5. FAA – Protein FASTA

FAA generally refers to a FASTA file containing protein or amino acid sequences.

A protein is a biological molecule that performs many important functions inside cells. Proteins are made from smaller molecules called amino acids.

A protein sequence may look like:

>Protein_001
MKTLLVAVAAAGT

The letters represent amino acids. Common examples include A = Alanine, R = Arginine, N = Asparagine, D = Aspartic acid, C = Cysteine, E = Glutamic acid, G = Glycine, H = Histidine, I = Isoleucine, L = Leucine, K = Lysine, M = Methionine, F = Phenylalanine, P = Proline, S = Serine, T = Threonine, W = Tryptophan, Y = Tyrosine, and V = Valine.

In simple terms: FAA = protein sequences stored in FASTA format.

6. Reference Genome – The Sequence Used for Comparison

A reference genome is a standard genome sequence used as a reference when analyzing a sample.

For example, if we sequence DNA from a person, we can compare the sequencing reads from that person with a human reference genome.

Two commonly encountered human reference genome versions are GRCh37 and GRCh38.

GRCh stands for Genome Reference Consortium Human.

The reference genome is usually provided as a FASTA file.

During sequencing analysis, the sample's reads can be compared with this reference to determine where they belong and whether there are differences.

7. GTF – Gene Transfer Format

GTF stands for Gene Transfer Format.

A GTF file contains information about genes and transcripts and their locations on the genome.

A gene is a section of DNA that contains information used by cells to produce a functional product, usually an RNA molecule and, in many cases, a protein.

A transcript is an RNA copy produced from a gene.

GTF files may contain information about genes, transcripts, exons, chromosomes, start positions, end positions, and DNA strand.

An exon is a part of a gene that remains in the mature RNA after RNA processing.

A GTF file may tell a bioinformatics program: This gene is located on this chromosome, between these positions, and contains these exons.

GTF files are widely used in RNA sequencing, gene expression analysis, transcriptome analysis, and genome annotation.

8. GFF and GFF3 – Genome Feature Information

GFF stands for General Feature Format.

GFF files are used to describe different features found in a genome.

These features may include genes, transcripts, exons, introns, coding regions, and regulatory regions.

An intron is a section of a gene that is removed from the RNA during RNA processing.

A coding region is a region of a gene that contains information used to produce a protein.

GFF3 is a commonly used version of the GFF format.

Both GTF and GFF are used for genome annotation, but their specifications and structures are different.

A simple way to remember them is:
GTF → commonly used for gene and transcript information
GFF/GFF3 → more general genomic feature information

9. BED – Browser Extensible Data

BED stands for Browser Extensible Data.

A BED file is used to describe specific regions of a genome.

For example:
chr1    1000    2000

This represents a region on chromosome 1 between the specified genomic coordinates.

A genomic coordinate is a position used to identify where something is located on a chromosome.

BED files can describe genes, exons, promoters, target regions, and regulatory regions.

A promoter is a DNA region near a gene that helps control when and where that gene is active.

BED files are frequently used with genome browsers and other bioinformatics tools.

10. SAM – Sequence Alignment/Map

SAM stands for Sequence Alignment/Map.

To understand SAM, we first need to understand alignment.

In bioinformatics, alignment means comparing a sequencing read with a reference sequence to determine where the read best matches.

For example, if a sequencing read is ATGCGT, a bioinformatics program can search the reference genome to find where this sequence belongs.

A SAM file stores information about these alignments. It can contain read name, chromosome, position, alignment information, mapping quality, sequence, and base quality.

SAM files are text-based files, meaning that their contents can generally be opened and read as text. However, large sequencing experiments can produce very large SAM files.

11. BAM – Binary Alignment Map

BAM is a binary version of SAM and is commonly used to store sequence alignment information.

A binary file stores information in a computer-efficient form rather than as ordinary readable text.

Because BAM is a compressed binary format, it is generally more storage-efficient than SAM.

A common workflow is:
FASTQ → Alignment → SAM/BAM

In practice, many bioinformatics tools directly generate BAM files.

BAM files are widely used for genome alignment, RNA sequencing analysis, variant calling, coverage analysis, and genome visualization.

12. CRAM – Compressed Reference-oriented Alignment Map

CRAM is a compressed format used for storing sequence alignment data.

It is designed to reduce the storage space required for alignment files.

CRAM can use the reference genome during compression, which allows the alignment information to be stored efficiently.

A simple comparison is:
SAM → text-based alignment file
BAM → binary alignment file
CRAM → highly compressed alignment file

CRAM is especially useful when working with large sequencing projects where storage space is an important consideration.

13. BAM Index – BAI

A BAM file is often accompanied by an index file.

One commonly used index format is BAI = BAM Index.

An index helps software quickly find information from a particular genomic region without reading the entire BAM file.

For example, if you want to view reads only from chromosome 1, the index helps the software locate that region quickly.

A typical pair is:
sample.bam
sample.bam.bai

14. VCF – Variant Call Format

VCF stands for Variant Call Format.

VCF is one of the most important formats in genome sequencing.

A variant is a difference between the DNA sequence of a sample and the reference genome.

For example, suppose the reference genome contains A but the sample contains G. This difference can be recorded as a genetic variant.

A VCF file stores information about such variants.

A simplified record may look like:
chr1    123456    .    A    G

This means that at a particular position on chromosome 1:
Reference base = A
Alternative base = G

The reference allele is the version present in the reference genome. The alternative allele is the different version observed in the sample.

A VCF file can also contain genotype, variant quality, read depth, allele frequency, filter status, and variant annotation.

15. Common Types of Genetic Variants

Single Nucleotide Variant (SNV): An SNV is a change involving a single DNA base, such as A → G at one genomic position.

Single Nucleotide Polymorphism (SNP): A SNP, pronounced “snip,” is a single-base variation that is commonly observed in a population. Not every SNV is necessarily considered a SNP; the term SNP is generally used for variants that meet population-frequency criteria.

Insertion: An insertion occurs when one or more DNA bases are added.

Deletion: A deletion occurs when one or more DNA bases are missing.

Structural Variant: A structural variant is a relatively large change in DNA structure. Examples include large insertions, large deletions, duplications, inversions, and translocations.

16. BCF – Binary Call Format

BCF stands for Binary Call Format.

BCF is a binary version of variant information represented in VCF-like form.

Because it is binary, it can be useful for efficient storage and processing of large variant datasets.

In simple terms:
VCF → text-based variant format
BCF → binary variant format

17. Variant Annotation – Understanding a Variant

Finding a variant is only one part of the analysis.

Researchers often need to know which gene contains the variant, whether the variant is inside an exon, whether it changes a protein, whether it has been reported before, whether it is common or rare, and whether it could have a biological effect.

The process of adding this information is called variant annotation.

Annotation means adding useful biological information to a sequence, genomic region, or variant.

For example, annotation can tell us that a variant is located in a particular gene, located in an exon, is a missense variant, has been reported in a database, or is predicted to affect a protein.

A missense variant is a DNA change that results in one amino acid being replaced by another amino acid in a protein.

Common variant annotation tools include VEP (Variant Effect Predictor), SnpEff, and ANNOVAR (ANNOtation of VAriants).

18. CSV – Comma-Separated Values

CSV stands for Comma-Separated Values.

It is a simple format for storing information in rows and columns.

Example:
Gene,Expression,Condition
TP53,25.6,Control
BRCA1,18.4,Treatment

Each comma separates one column from another.

CSV files are commonly used for gene expression results, sample information, statistical results, and analysis results.

CSV files can easily be opened using spreadsheet software such as Microsoft Excel.

19. TSV – Tab-Separated Values

TSV stands for Tab-Separated Values.

It is similar to CSV, but columns are separated by a tab rather than a comma.

TSV files are very common in bioinformatics because they can store large tables of biological information in a simple format.

In simple terms:
CSV → columns separated by commas
TSV → columns separated by tabs

20. Expression Count Matrix

In RNA sequencing (RNA-seq), researchers often want to measure how active different genes are.

RNA sequencing is a sequencing method used to study RNA molecules in a sample.

A gene expression count represents how many sequencing reads are associated with a particular gene or transcript.

These values can be stored in a count matrix.

A matrix is simply a table arranged in rows and columns.

Example:
Gene    Sample1    Sample2    Sample3
TP53    120        150        90
BRCA1   80         110        75
EGFR    300        250        320

Here:
Rows = genes
Columns = samples
Numbers = expression counts

Count matrices are commonly used for gene expression analysis, RNA-seq, differential expression analysis, and transcriptomics.

Differential expression analysis means identifying genes whose activity differs between two or more groups, such as treated and untreated samples.

21. MTX – Matrix Market Format

MTX refers to the Matrix Market format.

It is commonly used to store matrices, especially sparse matrices.

A sparse matrix is a table in which most values are zero.

This is common in single-cell RNA sequencing (scRNA-seq) because many genes may not be detected in every cell.

Instead of storing millions of zero values, a sparse format can store only the useful non-zero information.

A common single-cell dataset may contain:
matrix.mtx
barcodes.tsv
features.tsv

Here:
matrix.mtx = expression values
barcodes.tsv = cell identifiers
features.tsv = gene or feature information

A barcode is a short sequence or identifier used to distinguish one cell or biological sample from another.

22. HDF5 and H5AD

HDF5 stands for Hierarchical Data Format version 5.

It is a format designed for storing large and complex datasets efficiently. It can store multiple types of information inside a single file.

H5AD is a file format commonly used to store AnnData objects.

AnnData is a data structure widely used for analyzing single-cell and other high-dimensional biological datasets.

High-dimensional data means data containing a very large number of measurements.

An H5AD file may contain gene expression data, cell information, gene information, clustering results, dimensionality reduction results, and other analysis information.

H5AD files are commonly used in single-cell RNA-sequencing analysis.

23. GenBank Format

GenBank is a sequence data format used by the National Center for Biotechnology Information (NCBI).

NCBI is a major biological information resource maintained by the United States National Library of Medicine.

A GenBank record can contain both sequence information and biological annotation.

It may include DNA sequence, gene information, coding regions, protein information, organism information, and literature references.

This makes GenBank useful when researchers need sequence and annotation information together.

24. EMBL Format

The EMBL format is a sequence format associated with the European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI).

EMBL-EBI is a major European resource for biological data and bioinformatics.

An EMBL record can contain nucleotide sequence, gene information, coding regions, biological annotation, and references.

Like GenBank, it can store both sequence and annotation information.

25. PDB – Protein Data Bank

PDB stands for Protein Data Bank.

A PDB file contains information about the three-dimensional structure of biological molecules.

A three-dimensional structure describes how atoms are arranged in space.

PDB data can describe structures of proteins, DNA, RNA, and protein–ligand complexes.

A ligand is a molecule that binds to another molecule, such as a protein.

PDB files are widely used in structural bioinformatics, which is the study of biological molecules using their three-dimensional structures.

They can be used to study protein structure, binding sites, protein–ligand interactions, and molecular docking.

Molecular docking is a computational method used to predict how one molecule may fit and interact with another molecule.

26. PDBx/mmCIF

PDBx/mmCIF stands for Protein Data Bank Exchange / macromolecular Crystallographic Information File.

It is a modern format used to describe three-dimensional structures of biological molecules.

It provides a structured way of storing detailed molecular structure information.

It is particularly important for modern structural biology and is used by the Protein Data Bank.

27. Newick Format – Phylogenetic Trees

The Newick format is commonly used to store phylogenetic trees.

A phylogenetic tree is a diagram that represents evolutionary relationships between organisms, genes, or biological sequences.

For example:
(A,B,(C,D));

This represents relationships between four sequences or organisms.

Newick files are commonly used in evolutionary studies, phylogenetic analysis, and comparative genomics.

Common file extensions include .nw, .newick, and .tree.

28. Compressed Files – GZIP

Bioinformatics datasets can become extremely large.

To reduce their size, files are often compressed.

One common compression method is GZIP, which stands for GNU zip.

For example:
sample.fastq.gz

Here:
.fastq = the biological data format
.gz = the file has been compressed using GZIP

Similarly, sample.vcf.gz means that the VCF file has been compressed.

Compression reduces storage requirements and can make large datasets easier to transfer.

29. Paired-End FASTQ Files

Some sequencing experiments use paired-end sequencing.

In paired-end sequencing, both ends of the same DNA fragment are sequenced.

This commonly produces two FASTQ files:
sample_R1.fastq.gz
sample_R2.fastq.gz

Here:
R1 = Read 1
R2 = Read 2

The two files contain reads that belong to the same set of DNA fragments.

Paired-end sequencing can provide additional information and can be useful for genome alignment, RNA sequencing, variant detection, and structural variant analysis.

30. From FASTQ to the Final Analysis Report

A simplified sequencing workflow looks like this:

FASTQ
↓
Quality Control
↓
Cleaned FASTQ
↓
Alignment to Reference Genome
↓
SAM/BAM/CRAM
↓
Variant Calling
↓
VCF/BCF
↓
Variant Annotation
↓
Annotated Results
↓
Final Report

Let's understand each step.

Step 1: FASTQ – Starting Data

The sequencing machine produces millions or billions of sequencing reads. These reads are stored in FASTQ files.

For paired-end sequencing, we may have:
sample_R1.fastq.gz
sample_R2.fastq.gz

These files contain the raw sequencing information.

Step 2: Quality Control

Before starting detailed analysis, researchers need to check whether the sequencing data is good enough for further analysis.

This process is called Quality Control (QC).

Quality Control means checking the quality and reliability of the data.

Common things checked include number of reads, read quality, read length, GC content, adapter contamination, and duplicate sequences.

GC content is the percentage of bases in a sequence that are G or C.

Adapter contamination means unwanted artificial sequences from the sequencing process are present in the reads.

A commonly used tool for quality checking is FastQC.

FastQC can produce an HTML report containing graphs and quality information.

Step 3: Trimming and Filtering

If sequencing reads contain poor-quality regions or unwanted adapter sequences, these parts may need to be removed.

This process is called trimming.

Filtering means removing reads or parts of reads that do not meet selected quality requirements.

Common tools include Cutadapt, Trimmomatic, and fastp.

The result is usually another FASTQ file containing cleaner sequencing reads.

Step 4: Alignment to a Reference Genome

The cleaned sequencing reads can then be compared with a reference genome.

This process is called alignment.

Alignment determines where each sequencing read best matches the reference genome.

Common alignment tools include BWA (Burrows-Wheeler Aligner), Bowtie2, STAR (Spliced Transcripts Alignment to a Reference), and HISAT2 (Hierarchical Indexing for Spliced Alignment of Transcripts 2).

The output may be stored as SAM, BAM, or CRAM.

For example:
sample.bam

31. What Happens to the BAM File?

After alignment, the BAM file may be processed further.

Common steps include sorting, indexing, and coverage analysis.

Sorting means arranging reads according to their genomic positions.

Indexing means creating an index file so software can quickly access specific genomic regions.

Coverage analysis measures how many sequencing reads cover different parts of the genome.

Coverage means how many times a particular region of the genome has been sequenced.

For example, if a genomic position has been read 30 times, its sequencing depth is approximately 30×.

32. Variant Calling

If the purpose of the sequencing experiment is to identify genetic differences, the aligned reads can be analyzed for variants.

This process is called variant calling.

Variant calling identifies positions where the sample appears to differ from the reference genome.

The result is usually stored in:
sample.vcf
or
sample.vcf.gz

The VCF can then be filtered and annotated.

33. Variant Filtering

Not every detected variant is necessarily reliable.

Some variants may be supported by only a small number of reads or may have other quality concerns.

Variant filtering means selecting variants that meet defined quality requirements.

Filtering may consider read depth, variant quality, genotype quality, allele frequency, and other quality measures.

The result may be a smaller set of higher-confidence variants.

34. Variant Annotation

After variant calling and filtering, researchers usually want to understand what the variants mean biologically.

This is where variant annotation becomes important.

Annotation can provide information such as gene name, transcript, location within a gene, variant type, protein change, population frequency, known disease associations, and predicted effect.

A population frequency tells us how often a particular variant is observed in a population.

A protein change describes whether a DNA variant changes the amino acid sequence of a protein.

The final annotated information may be stored in VCF, TSV, CSV, Excel-compatible tables, or other report formats.

35. What Is the Final Report?

The final report depends on the type of sequencing experiment.

For example, an NGS analysis report may contain:

Sample information: Sample ID, sample type, sequencing platform, and experimental information.

Sequencing quality: Number of reads, average read quality, read length, percentage of reads aligned, and coverage.

Variant information: Chromosome, genomic position, reference allele, alternative allele, gene, variant type, and genotype.

Annotation information: Variant consequence, population frequency, database information, and functional prediction.

Interpretation: The report may summarize the important findings of the analysis.

For research studies, the final report may help researchers decide which genes, variants, or biological processes require further investigation.

For clinical applications, reporting and interpretation must follow appropriate laboratory, clinical, and regulatory standards.

36. Quick Guide to Common Bioinformatics File Formats

FASTQ – Sequence + quality format – Reads + quality scores – Raw sequencing data
FASTA – Sequence format – DNA/RNA/protein sequences – Reference and biological sequences
FNA – Nucleotide FASTA – Nucleotide sequences – Genomes and DNA sequences
FAA – Amino acid FASTA – Protein sequences – Protein analysis
GTF – Gene Transfer Format – Gene/transcript information – Genome annotation
GFF/GFF3 – General Feature Format – Genomic features – Genome annotation
BED – Browser Extensible Data – Genomic regions – Regions and genome browsers
SAM – Sequence Alignment/Map – Read alignments – Alignment data
BAM – Binary Alignment Map – Binary read alignments – Alignment analysis
CRAM – Compressed alignment format – Compressed alignments – Large sequencing datasets
VCF – Variant Call Format – Genetic variants – Variant analysis
BCF – Binary Call Format – Binary variant data – Efficient variant processing
CSV – Comma-Separated Values – Tables – Results and sample information
TSV – Tab-Separated Values – Tables – Bioinformatics results
MTX – Matrix Market – Matrix data – Single-cell datasets
HDF5 – Hierarchical Data Format 5 – Large structured data – Complex biological datasets
H5AD – AnnData file format – Single-cell data – Single-cell analysis
GenBank – GenBank sequence format – Sequence + annotation – Sequence databases
EMBL – EMBL sequence format – Sequence + annotation – Sequence databases
PDB – Protein Data Bank format – 3D molecular structure – Structural biology
PDBx/mmCIF – Protein Data Bank Exchange / macromolecular Crystallographic Information File – Detailed molecular structures – Structural biology
Newick – Phylogenetic tree format – Evolutionary trees – Phylogenetic analysis
GZIP (.gz) – GNU zip compression – Compressed files – Reducing file size

37. Which Formats Should a Beginner Remember First?

There are many bioinformatics file formats, but beginners do not need to memorize all of them at once.

Start with these:

DNA sequencing: FASTQ → raw sequencing reads

Biological sequences: FASTA → DNA, RNA, or protein sequences

Genome annotation: GTF/GFF → genes and genomic features

Genomic regions: BED → specific regions of a genome

Alignment: SAM/BAM/CRAM → sequencing reads aligned to a reference

Variants: VCF/BCF → genetic variants

Tables: CSV/TSV → results organized in rows and columns

Single-cell data: MTX/H5AD → single-cell datasets

Protein structures: PDB/PDBx/mmCIF → three-dimensional molecular structures

Evolution: Newick → phylogenetic trees

38. The Big Picture

It can be easier to remember bioinformatics file formats by thinking about the question each file answers.

FASTQ asks: What sequences did the sequencing machine produce, and how good are those sequences?

FASTA asks: What is the sequence?

GTF/GFF asks: Where are the genes and other features in the genome?

SAM/BAM/CRAM asks: Where do the sequencing reads match the reference genome?

VCF asks: What genetic differences were found?

Annotation files ask: What do these genetic differences potentially mean?

Final report asks: What are the important findings from the entire analysis?

Conclusion

Bioinformatics involves many different types of biological data, and each type of data may use a different file format.

A typical sequencing analysis can move through several formats:

FASTQ → Quality Control → Cleaned FASTQ → SAM/BAM/CRAM → VCF → Variant Annotation → Tables → Final Report

FASTQ stores sequencing reads and their quality information. FASTA stores biological sequences. FNA and FAA are commonly used for nucleotide and protein FASTA sequences. GTF and GFF describe genes and other genomic features. BED describes genomic regions. SAM, BAM, and CRAM store information about sequencing reads aligned to a reference genome. VCF and BCF store genetic variants.

Other formats are important for specialized analyses. MTX and H5AD are widely used in single-cell analysis, PDB and PDBx/mmCIF are used for molecular structures, and Newick is used for phylogenetic trees.

The most important thing is not simply memorizing file extensions. It is understanding what information each file contains, where the file comes from, which tools use it, and what happens to the data in the next step.

Once these concepts become familiar, bioinformatics workflows become much easier to understand.

From a sequencing machine's first raw reads to the final biological result, file formats are the language that allows different bioinformatics tools and analysis steps to communicate with one another.

Explore Genomic Testing with GenoConnect

Understanding the difference between WGS and WES is an important first step when exploring genomic testing.

At GenoConnect, you can explore sequencing options designed to provide access to detailed genetic data and reports. Depending on the option you choose, you can explore different levels of genomic coverage and analysis.

Whether your interest is ancestry, family history, genetic variation, or research, understanding what your sequencing data contains can help you make more informed choices about your genetic information.

1. Before choosing a sequencing option, consider:

2. The genomic regions covered

3. Sequencing depth and coverage

4. The type of data provided

5. Available reports and analysis

6. Ancestry and population reference resources

7. Raw data access

8. Data privacy and storage policies

9. Potential for future data analysis

 What Can You Actually Do With Your Raw Genetic Data?

At the end of the day, you don’t need to be a bioinformatician to get real value from your DNA. Most everyday users recognize and rely on two standard formats: 23andMe and AncestryDNA. These are simple, human-readable plain text files containing your genotype information—making them the universal key accepted by popular third-party analysis platforms like GEDmatch and IllustrativeDNA for in-depth ancestry and ancient breakdown reports. 

What Data Does GenoConnect Provide?

We believe in complete data transparency and giving you files that you can actually use. That is why all GenoConnect raw data is generated on the industry-standard GRCh37 (hg19) human reference assembly:

Whole Genome Sequencing (WGS):

You receive both deep research-grade files and universal consumer files:

GRCh37 23andMe format (.txt) – Ready for immediate upload to GEDmatch, IllustrativeDNA, and third-party tools.

FASTQ (.fastq.gz) – Complete raw sequencing reads and quality metrics. 

BAM (.bam) – Your aligned sequencing data. 

VCF (.vcf) – Comprehensive list of your detected genetic variants. 

Global Screening Array (GSA / Microarray):

GRCh37 23andMe format (.txt) – Lightweight, compatible text file.

IDAT (.idat) – Raw Illumina scanner intensity files.

VCF (.vcf) – Standardized genotype calls. 

Can Raw DNA Data Be Converted Between Formats?

Yes, raw data conversion is entirely possible.

 If you have complex sequencing files (like VCF or BAM) or alternative array data, specialized algorithms can parse and extract your genotype coordinates to generate a clean, standard 23andMe-style plain text file. To save you the trouble of using complex command-line software, GenoConnect offers a Raw Data Converter as a built-in service.

 Convert and Impute Your DNA Files with GenoConnect

You don’t need to write code or run terminal tools. Under Tools → Raw data converter, our platform handles format conversion and data density enhancement:

 Format Converter: Upload IDAT, FASTQ, BAM, CRAM, VCF, PLINK, or 23andMe-style text files. We convert them into formats you can actually use across external tools—calibrated on GRCh37 or GRCh38. 

Imputation: Array files only cover a slice of the genome. Imputation infers additional SNPs from a reference panel so downstream tools see a denser dataset.

Risk-Free Processing: You upload your file or choose an existing kit, our team verifies whether the conversion is feasible, and you are only charged if the request can be completed.

Ready to Explore?

Whether you want to convert an existing file or explore deeper insights from another testing provider, you don’t need to buy a brand-new kit to get started:

Convert or Impute Your Files: Raw Data Converter | Convert & Impute DNA Files | GenoConnect

Upload Existing Kit Data: Go to [Tools] → [Upload Raw DNA Data] on the top menu to import files from 23andMe, AncestryDNA, FamilyTreeDNA, MyHeritage, LivingDNA, or other providers (.txt, .csv, .zip, .vcf).