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Protocols

SeqGL specifications

Information


Unique identifier OMICS_13752
Name SeqGL
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.1.4
Stability Stable
Requirements
Biostrings, parallel, ChIPKernels, GenomicRanges, Matrix, spams, WGCNA, fastcluster
Maintained Yes

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Maintainer


  • person_outline Christina Leslie <>

Publication for SeqGL

SeqGL citations

 (5)
library_books

Chromatin Accessibility Based Characterization of the Gene Regulatory Network Underlying Plasmodium falciparum Blood Stage Development

2018
PMCID: 5899830
PMID: 29649445
DOI: 10.1016/j.chom.2018.03.007

[…] that are bound by specific tfs in a stage-specific manner. to identify dna motifs that could perform this function, we first performed an exhaustive de novo motif search using gimmemotifs and seqgl (; , ). these de novo predicted motifs were combined with previously predicted plasmodium motifs () and known vertebrate, invertebrate, and plant motifs from the cis-bp database (), yielding […]

library_books

Deconvolving sequence features that discriminate between overlapping regulatory annotations

2017
PMCID: 5663517
PMID: 29049320
DOI: 10.1371/journal.pcbi.1005795

[…] features associated with cell type-specific tf binding across two cell types, along with features shared by tf binding sites in both cell types. the group lasso based logistic regression classifier seqgl [] also implements a similar multi-task framework to identify features that are discriminative between two classes and features that are common to both. no existing discriminative feature […]

call_split

Direct AUC optimization of regulatory motifs

2017
PMCID: 5870558
PMID: 28881989
DOI: 10.1093/bioinformatics/btx255
call_split See protocol

[…] tf binding. for example, in k-mer-based svm models, there can be a large number of very similar k-mer features that are all significant for the prediction task (). to deal with such difficulties, seqgl () and mil () similarly adopt a dml method (homer) to interpret their outputs, while gkmsvm () would cluster k-mers into pwms for further analysis, which could be viewed as a simplified version […]

library_books

Discovery and validation of information theory based transcription factor and cofactor binding site motifs

2016
PMCID: 5389469
PMID: 27899659
DOI: 10.1093/nar/gkw1036

[…] pipeline with other motif discovery tools from two perspectives of revealing primary and cofactor binding motifs (). meme-chip was previously used to derive motifs for 457 chip-seq datasets () and seqgl () was used to analyze 105 datasets. among the sequence-specific tfs (n = 98) investigated by both tools, maskminent and meme-chip discovered primary motifs for 80 (∼81.6%) and 92 (∼93.9%) tfs, […]

library_books

Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation

2015
PMCID: 4626279
PMID: 26390058
DOI: 10.1038/ng.3402

[…] could predict expression changes of genes in cell state transitions from the dna sequence signals and lineage history of their active enhancers. to dissect sequence motifs in enhancers, we applied seqgl, a group lasso algorithm we developed to identify multiple dna signals de novo from dnase data using k-mer patterns. seqgl first computes a count matrix of occurrences of k-mers in dnase peak […]


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SeqGL institution(s)
Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
SeqGL funding source(s)
This work was supported by NHGRI award R01-HG006798.

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