DeepSEA statistics

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Citations per year

Number of citations per year for the bioinformatics software tool DeepSEA
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Tool usage distribution map

This map represents all the scientific publications referring to DeepSEA per scientific context
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Associated diseases

This word cloud represents DeepSEA usage per disease context
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Protocols

DeepSEA specifications

Information


Unique identifier OMICS_13506
Name DeepSEA
Interface Web user interface
Restrictions to use None
Input data DNA sequence, chromatin regions-hg19 coordinates, variants sequence
Input format FASTA, BED, VCF
Output format ZIP, tab-delimited file
Computer skills Basic
Stability Stable
Source code URL http://deepsea.princeton.edu/media/code/deepsea.v0.94.tar.gz
Maintained Yes

Documentation


Maintainer


  • person_outline Olga G. Troyanskaya

Information


Unique identifier OMICS_13506
Name DeepSEA
Software type Application/Script
Interface Command line interface
Restrictions to use None
Input data DNA sequence, chromatin regions-hg19 coordinates, variants sequence
Input format FASTA, BED, VCF
Output data DNA sequence feature predictions, variant effect predictions
Output format ZIP, tab-delimited file
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Olga G. Troyanskaya

Publication for DeepSEA

DeepSEA citations

 (51)
library_books

Prediction of enhancer promoter interactions via natural language processing

2018
BMC Genomics
PMCID: 5954283
PMID: 29764360
DOI: 10.1186/s12864-018-4459-6

[…] tural language processing (NLP). As is well known, Convolutional Neural Network (CNN) is a powerful deep learning model in computer vision area. Inspired by deep learning applied in image processing, DeepSEA and DeepBind first regard DNA sequences as binary images through one-hot encoding. They both preprocess the DNA sequences by transforming them into 4xL images (L is the length of a sequence), […]

library_books

Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity related genes from GWAS

2018
Nat Commun
PMCID: 5904163
PMID: 29666371
DOI: 10.1038/s41467-018-03554-9

[…] analysis with all DHSs not found in adipocyte chromosomal interactions as the background input.We also assessed predicted differential TF binding using the tool deep learning–based sequence analyzer (DeepSEA), which assesses differential histone modification, TF binding, and DHS profiles using a deep learning-based algorithmic approach and gives a functional significance score at the single nucleo […]

call_split

Overexpression of a type III PKS gene affording novel violapyrones with enhanced anti influenza A virus activity

2018
Microb Cell Fact
PMCID: 5898002
PMID: 29650021
DOI: 10.1186/s12934-018-0908-9
call_split See protocol

[…] e : Table S1. Escherichia coli DH5α was used as the host for general subcloning []. E. coli ET12567/pUZ8002 [] was used as the cosmid donor host for E. coli–Streptomyces intergeneric conjugation. The deepsea-derived S. somaliensis SCSIO ZH66 has been described previously [, ]. S. coelicolor M1146 [] and S. sanyensis FMA [] were used as the host strains for heterologous expression. Plasmid extracti […]

library_books

Opportunities and obstacles for deep learning in biology and medicine

2018
PMCID: 5938574
PMID: 29618526
DOI: 10.1098/rsif.2017.0387

[…] atasets.Multi-task learning is an approach related to transfer learning. In a multi-task learning framework, a model learns a number of tasks simultaneously such that features are shared across them. DeepSEA [] implemented multi-task joint learning of diverse chromatin factors from raw DNA sequence. This allowed a sequence feature that was effective in recognizing binding of a specific TF to be si […]

library_books

Presence of a Haloarchaeal Halorhodopsin Like Cl− Pump in Marine Bacteria

2018
PMCID: 5877348
PMID: 29553064
DOI: 10.1264/jsme2.ME17197

[…] 2 were 73% (RmHR-R28HR1), 62% (RmHR-R28HR2), and 57% (R28HR1-R28HR2) respectively. No rhodopsin gene was found in the genome of R. profundi SAORIC-476T. Although R28HR1 and R28HR2 were found from the deepsea isolate, it currently remains unclear whether these are deep-sea adapted rhodopsins because the habitat of the genus Rubrivirga is not yet understood. In the BLASTP search against the metageno […]

library_books

Common α globin variants modify hematologic and other clinical phenotypes in sickle cell trait and disease

2018
PLoS Genet
PMCID: 5891078
PMID: 29590102
DOI: 10.1371/journal.pgen.1007293

[…] vity (DHS) data from multiple cell lines (the sum of the counts for each allele across 46 heterozygous cell types) and erythroblasts were used to assess allelic skew in sequenced reads.[, ] EIGEN-PC, deepSEA, and gkm-SVM are algorithms that predict the function of non-coding variants and were used as previously described to in silico predict the effect of common variants.[, , , ] In silico mutagen […]


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DeepSEA institution(s)
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA; Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA; Simons Center for Data Analysis, Simons Foundation, New York, NY, USA
DeepSEA funding source(s)
This work was primarily supported by US National Institutes of Health (NIH) grants R01 GM071966 and R01 HG005998; and in part by the US National Science Foundation (NSF) CAREER award (DBI-0546275), NIH award T32 HG003284 and NIH grant P50 GM071508 and by the Genetic Networks program of the Canadian Institute for Advanced Research (CIFAR).

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