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chevron_left Insertion detection Retroviral vector integration site detection CNV detection chevron_right
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Seeksv specifications

Information


Unique identifier OMICS_12851
Name Seeksv
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input format BAM
Operating system Unix/Linux
Programming languages C++
Computer skills Advanced
Stability Stable
Maintained Yes

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  • person_outline Liao BQ <>

Publication for Seeksv

Seeksv in publications

 (3)
PMCID: 4838165
PMID: 27035118
DOI: 10.3892/mmr.2016.5014

[…] single nucleotide variants (snvs) were detected using varscan () and somatic indels were detected using gatk. structure variants (svs) and somatic cnvs were identified using breakdancer ()/crest/seeksv (self-method) and a self-method based on the segseq () algorithm, respectively. virus integration sites were identified using a self-method based on unmapped reads. the procedure also included […]

PMCID: 4776238
PMID: 26936516
DOI: 10.1038/srep22338

[…] which is capable of reaching sensitivity and specificity beyond 99% for mutant allelic frequencies >0.03 () with q30 reads. cnvs were detected using contra, and gene arrangement is detected by seeksv. without a matched normal, interpretation focused on known somatic hotspot mutations, including snvs, indels, as well as known cnvs and gene arrangements in lung cancer. the mutations […]

PMCID: 4717154
PMID: 25823027
DOI: 10.1038/onc.2015.72

[…] clusters with less than five supporting read pairs or falling to ucsc simple repeat regions were discarded. to identify the exact sv breakpoints at single nucleotide level, an in-house program seeksv was used. similar to crest, seeksv used next-generation short reads with partial alignments with the reference genome to call svs and included four steps as follows: (i) obtain soft-clipped […]


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Seeksv institution(s)
College of Information Science and Engineering, Hunan University, Changsha, China; Beijing Genomics Institute, Shenzhen, Guangdong, China; Department of Computer Science State University of New York, New Paltz, NY, USA
Seeksv funding source(s)
This project is supported by the Program for New Century Excellent Talents in University (Grant No. NCET (10(0365), and the National Nature Science Foundation of China (Grant Nos. 11171369, 61272395, 61370171, 61300128, 61472127 and 61572178).

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