<p>A description of the PLINK file formats can be found in the File format reference section of the PLINK 2.0 manual:</p>
<p><a href="https://www.cog-genomics.org/plink/2.0/formats" rel="nofollow noreferrer">https://www.cog-genomics.org/plink/2.0/formats</a></p>
<p>This does not include a description about bed/bim/fam; that is in the PLINK 1.9 manual:</p>
<p><a href="https://www.cog-genomics.org/plink/1.9/formats#bed" rel="nofollow noreferrer">https://www.cog-genomics.org/plink/1.9/formats#bed</a></p>
<p>To summarise, the PLINK bed/bim/fam file formats describe the following three things:</p>
<ol>
<li><strong>bed</strong> - binary (i.e. non-human-readable) representation of genotype calls; essentially a mapping of (individual ID, variant ID) -&gt; (genotype) [not to be confused with <a href="https://en.wikipedia.org/wiki/BED_(file_format)" rel="nofollow noreferrer">BED files</a>]</li>
<li><strong>bim</strong> - location information for variants; essentially a mapping of (variant ID) -&gt; (location)</li>
<li><strong>fam</strong> - individual metadata, especially family relationships; essentially a mapping of (individual ID) -&gt; (phenotype, family)</li>
</ol>
<p>Notably, these file formats do not include base context that surrounds the variant information. Because it's not clear what has been filtered out, they can't be used to create accurate DNA sequences, even when using the reference sequences as a template. However, the lack of spurious context means that the format should be easier to manage for machine learning applications - especially those that work with matrix-like formats.</p>
<p>However, if you want to use this data for purposes outside of PLINK tools, you'd be better off using PLINK to convert it to <a href="https://en.wikipedia.org/wiki/Variant_Call_Format" rel="nofollow noreferrer">VCF format</a>, which is the primary data format for PLINK 2, and a format that is widely used elsewhere.</p>

CC BY-SA 4.0
2024-08-05T19:07:53.627Z
For what I got so far, the files contain the information about alleles in some positions in the genome for a set of individuals, along with the corresponding alleles from the reference (In the case of the link in the question, Hg19). So it could be used to infer things like damage profile, or a consensus based on the reference? Is my interpretation correct?
dyxcvi
CC BY-SA 4.0
2024-08-05T21:24:43.943Z
@dyxcvi I've added a bit more detail. The main thing I want to get across is that variant-only information shouldn't be used to generate sequences (including consensus sequences), but it can be used to generate consensus genotypes (or minor/major allele frequencies).
gringer
CC BY-SA 4.0
2024-08-04T00:44:54.863Z
2024-08-05T21:21:34.063Z
2024-08-05T21:21:34.063Z
gringer
<p>I'm new to bioinformatics, and in my first experiments I've always used FASTA/FASTQ files for my analysis. What I read is that in general terms FASTQs are used for reads and FASTAs for reference sequences.
I'm now dealing with a new data type from the AADR (<a href="https://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-data" rel="nofollow noreferrer">https://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-data</a>), which comes in <em>packedancestrymap</em> format. I'm able to convert it to a format I can use in Plink2 (It uses .bed, .bim, and .fam files), but I'm not sure of how to handle this data.
In the AADR's link, it says that one of the files contains the genotypes of the DNA data.</p>
<ol>
<li>What does that mean? Is it analogous to sequences or reads, or is it a different kind of information?</li>
<li>Can I do alignments with that data? If positive, I'd like to align it to a reference mtDNA FASTA to generate more data for an experiment I'm conducting, or run some damage analysis in it to generate features that would feed a machine learning model.</li>
</ol>
<p>Thank you in advance.</p>

<phylogenetics><plink>
Difference between FASTA/FASTQ formats and Plink2 PACKEDPED format
# Difference between FASTA/FASTQ formats and Plink2 PACKEDPED format

(Asked by: username_0 on 2024-08-03)

I'm new to bioinformatics, and in my first experiments I've always used FASTA/FASTQ files for my analysis. What I read is that in general terms FASTQs are used for reads and FASTAs for reference sequences.
I'm now dealing with a new data type from the AADR (<https://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-data>), which comes in *packedancestrymap* format. I'm able to convert it to a format I can use in Plink2 (It uses .bed, .bim, and .fam files), but I'm not sure of how to handle this data.
In the AADR's link, it says that one of the files contains the genotypes of the DNA data.

1. What does that mean? Is it analogous to sequences or reads, or is it a different kind of information?
2. Can I do alignments with that data? If positive, I'd like to align it to a reference mtDNA FASTA to generate more data for an experiment I'm conducting, or run some damage analysis in it to generate features that would feed a machine learning model.

Thank you in advance.

**Tags:** phylogenetics, plink

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## 1 answers

### Answer by username_1 on 2024-08-04. Score: 0 | Accepted answer

A description of the PLINK file formats can be found in the File format reference section of the PLINK 2.0 manual:

<https://www.cog-genomics.org/plink/2.0/formats>

This does not include a description about bed/bim/fam; that is in the PLINK 1.9 manual:

<https://www.cog-genomics.org/plink/1.9/formats#bed>

To summarise, the PLINK bed/bim/fam file formats describe the following three things:

1. **bed** - binary (i.e. non-human-readable) representation of genotype calls; essentially a mapping of (individual ID, variant ID) -> (genotype) [not to be confused with [BED files](https://en.wikipedia.org/wiki/BED_(file_format))]
2. **bim** - location information for variants; essentially a mapping of (variant ID) -> (location)
3. **fam** - individual metadata, especially family relationships; essentially a mapping of (individual ID) -> (phenotype, family)

Notably, these file formats do not include base context that surrounds the variant information. Because it's not clear what has been filtered out, they can't be used to create accurate DNA sequences, even when using the reference sequences as a template. However, the lack of spurious context means that the format should be easier to manage for machine learning applications - especially those that work with matrix-like formats.

However, if you want to use this data for purposes outside of PLINK tools, you'd be better off using PLINK to convert it to [VCF format](https://en.wikipedia.org/wiki/Variant_Call_Format), which is the primary data format for PLINK 2, and a format that is widely used elsewhere.

> **Comments:**
> **username_0**: For what I got so far, the files contain the information about alleles in some positions in the genome for a set of individuals, along with the corresponding alleles from the reference (In the case of the link in the question, Hg19). So it could be used to infer things like damage profile, or a consensus based on the reference? Is my interpretation correct?  
> **username_1**: @dyxcvi I've added a bit more detail. The main thing I want to get across is that variant-only information shouldn't be used to generate sequences (including consensus sequences), but it can be used to generate consensus genotypes (or minor/major allele frequencies).  

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