Sei
Introduction
Sei is a deep-learning-based framework for systematically predicting sequence regulatory activities and applying sequence information to understand human genetics data. Sei provides a global map from any sequence to regulatory activities, as represented by 40 sequence classes. Each sequence class integrates predictions for 21,907 chromatin profiles (transcription factor, histone marks, and chromatin accessibility profiles across a wide range of cell types) from the underlying Sei deep learning model. Importantly, this framework is trained without using any variant data, allowing it to predict the regulatory impact of any variant, including rare or previously unseen ones.
Sei is described in the following manuscript: Kathleen M. Chen, Aaron K. Wong, Olga G. Troyanskaya and Jian Zhou, A sequence-based global map of regulatory activity for deciphering human genetics. Nature Genetics (2022).
The Sei code repository can be found here.
For older DeepSEA models see: Beluga (DeepSEA) (2019)
Input
We support three types of input: VCF, FASTA, BED. If you want to predict effects of noncoding variants, use VCF format input. If you want to predict chromatin feature probabilities for DNA sequences, use FASTA format. If you want to specify sequences from the human reference genome, you can use BED format. See below for a quick introduction:
VCF format is used for specifying a genomic variant. A minimal example is chr1 109817590 - G T (if you want to copy this text as input, you will need to change spaces to tabs). The five columns are chromosome, position, name, reference allele, and alternative allele.
FASTA format input should include sequences of 4096bp length each. If a sequence is different from 4096bp:
Note: The prediction is for the center base of the input sequence
Longer sequences: Only the center 4096bp will be used
Shorter sequences: Sequences shorter than 4096bp will be padded with ‘N’ bases evenly on both sides
Important: We do not recommend using FASTA input smaller than 4096bp unless it is very close (only a few bp off)
Note: This padding behavior is not recommended. N’s were extremely rare in training data (only appearing in assembly gaps), and the model has not been evaluated with artificially padded sequences
Strong recommendation: Always provide sequences of exactly 4096bp by including genomic flanking sequences
BED format provides another way to specify sequences in human reference genome. A minimal example is chr5 134871851 134871852. The three columns are chromosome, start position, and end position.
Large submissions
We recommend using the web server if you have <10,000 variants or sequences. You will experience degraded performance when submitting a larger set of sequences. In those instances, we suggest that you split the set into multiple <10,000 submissions, or run the standalone version on your local machine, or contact our group directly.
Output
Sequence classes
The Sei framework predicts 40 sequence class scores, covering a wide range of regulatory activities such as cell-type-specific enhancers and promoters, as well as 21,907 chromatin profiles for any DNA sequence. Sequence class-level variant effects are computed by comparing the predictions for the reference and the alternative alleles. A positive score indicates an increase in sequence class activity by the alternative allele and vice versa. Sequence class-level scores are computed by projecting the 21,907 chromatin profile predictions for the sequence to the unit vector that represents each sequence class.A full description of how Sei sequence scores are computed can be found in the Sei paper (2022).
To help interpretation, we grouped sequence classes into groups including P (Promoter), E (Enhancer), CTCF (CTCF-cohesin binding), TF (TF binding), PC (Polycomb-repressed), HET (Heterochromatin), TN (Transcription), and L (Low Signal) sequence classes. Please refer to our manuscript for a more detailed description of the sequence classes.
Note: sequence class predictions are only available for vcf inputs.
| Sequence class label | Sequence class name | Rank by size | Group |
|---------------------:|----------------------------------:|-------------:|------:|
| PC1 | Polycomb / Heterochromatin | 0 | PC |
| L1 | Low signal | 1 | L |
| TN1 | Transcription | 2 | TN |
| TN2 | Transcription | 3 | TN |
| L2 | Low signal | 4 | L |
| E1 | Stem cell | 5 | E |
| E2 | Multi-tissue | 6 | E |
| E3 | Brain / Melanocyte | 7 | E |
| L3 | Low signal | 8 | L |
| E4 | Multi-tissue | 9 | E |
| TF1 | NANOG / FOXA1 | 10 | TF |
| HET1 | Heterochromatin | 11 | HET |
| E5 | B-cell-like | 12 | E |
| E6 | Weak epithelial | 13 | E |
| TF2 | CEBPB | 14 | TF |
| PC2 | Weak Polycomb | 15 | PC |
| E7 | Monocyte / Macrophage | 16 | E |
| E8 | Weak multi-tissue | 17 | E |
| L4 | Low signal | 18 | L |
| TF3 | FOXA1 / AR / ESR1 | 19 | TF |
| PC3 | Polycomb | 20 | PC |
| TN3 | Transcription | 21 | TN |
| L5 | Low signal | 22 | L |
| HET2 | Heterochromatin | 23 | HET |
| L6 | Low signal | 24 | L |
| P | Promoter | 25 | P |
| E9 | Liver / Intestine | 26 | E |
| CTCF | CTCF-Cohesin | 27 | CTCF |
| TN4 | Transcription | 28 | TN |
| HET3 | Heterochromatin | 29 | HET |
| E10 | Brain | 30 | E |
| TF4 | OTX2 | 31 | TF |
| HET4 | Heterochromatin | 32 | HET |
| L7 | Low signal | 33 | L |
| PC4 | Polycomb / Bivalent stem cell Enh | 34 | PC |
| HET5 | Centromere | 35 | HET |
| E11 | T-cell | 36 | E |
| TF5 | AR | 37 | TF |
| E12 | Erythroblast-like | 38 | E |
| HET6 | Centromere | 39 | HET |
Regulatory feature scores
diffs: The difference between the the predicted probability of the reference allele and the alternative allele for a regulatory feature (\(p_{alt} -p_{ref}\)).