HumanBase Sei: a sequence-based global map of regulatory activity for deciphering human genetics

Sei analysis
Sei is a deep-learning-based framework for systematically predicting sequence regulatory activities and applying sequence information to understand human genetics data.

New sequence model

Sei provides a global map from any sequence to regulatory activities, as represented by 40 sequence classes by integrating predictions for 21,907 chromatin profiles. You can also find the Sei code repository here or read about our manuscript here. See for more details.

Submit a Sei analysis

Model:

Sei (2021 DNA model)

Type:

VCF

Genome assembly:

GRCh37/hg19

Show format descriptions

VCF (Variant effect prediction)

Upload a VCF file for variant effect prediction. VCF format:

Each line must have at least 5 tab separated fields.
If there is a header column, it must begin with a hash `#` and the first 5 tab separated entries must be the following: `#CHROM`, `POS`, `ID`, `REF`, `ALT`
The first column: Chromosome name
The second column: Position. A positive integer within chromosome length
The third column: Variant name. This field can be left empty.
The fourth column: Reference allele. One or more upper case codes from ATCG. Maximum 100 characters.
The fifth column: Alternative allele. Contain one or more upper case codes from ATCG. Maximum 100 characters.
Required for Seqweaver - The sixth column: Strand. Either '+' or '-' indicating strand.

A VCF file can be opened in any text editor. However, for most users a VCF file is best viewed using a spreadsheet program such as Microsoft Excel or Google Sheets.

Example VCF file
Example VCF file with strand

BED (Chromatin feature prediction)

Upload a BED file for chromatin feature prediction. BED format:

Each line has at least 3 tab separated fields.
The first column: Chromosome name
The second column: Start Position. A non-negative integer within chromosome length
The third column: End Position. A non-negative integer within chromosome length
Center Position Analysis: We analyze the center position of the interval. For even-length intervals, we use the left-center position (floor division). Examples:
- chr1 100 100 (equal coordinates) → center at position 100
- chr1 100 101 (single base) → center at position 100
- chr1 100 105 (5 bases) → center at position 102
- chr1 100 106 (6 bases) → center at position 103 (left-center)

Example BED file

FASTA (chromatin feature prediction / in-silico mutagenesis)

Upload a FASTA file for chromatin feature prediction or in-silico mutagenesis. FASTA format:

Each section starts with ">".
Sequence length depends on the selected model: Sei (4096bp), Beluga (2000bp), or Seqweaver (1000bp)
Allowed nucleic acid codes: ATCGatcg (lowercase is treated the same of uppercase).

Example FASTA file

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Example vcf submission

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Sei is a deep-learning-based framework for systematically predicting sequence regulatory activities and applying sequence information to understand human genetics data.

Visitfor additional help and details.

Flatiron Institute - Simons Foundation | How to Cite

Sei analysisSei is a deep-learning-based framework for systematically predicting sequence regulatory activities and applying sequence information to understand human genetics data.

Submit a Sei analysis

VCF (Variant effect prediction)

BED (Chromatin feature prediction)

FASTA (chromatin feature prediction / in-silico mutagenesis)

Sei analysis
Sei is a deep-learning-based framework for systematically predicting sequence regulatory activities and applying sequence information to understand human genetics data.