TagCleaner protocols

TagCleaner specifications

Information


Unique identifier OMICS_01094
Name TagCleaner
Software type Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Input data Some data containing the metagenomic reads.
Input format FASTA,FASTQ
Operating system Unix/Linux
Computer skills Advanced
Version 0.16
Stability Stable
Maintained Yes

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Publication for TagCleaner

TagCleaner IN pipelines

 (4)
2018
PMCID: 5795050
PMID: 29321239
DOI: 10.1098/rsob.170184

[…] of paired-end (76 bp) illumina gaiix at the exeter sequencing service producing 2× 212 760 559 hiseq reads along with 2× 15 266 599 and 2× 16 274 715 gaiix reads. after trimming and cleaning (using tagcleaner [128] and prinseq [129]) of the data, we subsequently digitally normalized it with khmer [130] in order to discard redundant data and sampling variation and remove errors. this reduced […]

2017
PMCID: 5310281
PMID: 28198672
DOI: 10.1186/s12864-016-3254-5

[…] the similarity of marker species abundance profiles; and (6) network analysis, in which co-occurrence correlation networks based on different periodontitis states were inferred and compared.fig. 1 , tagcleaner [22] was used to remove sequencing tags. tags were predicted by tagcleaner with coverage over 50%. read sequences at either end representing tags without mismatches were removed. prinseq […]

2017
PMCID: 5310281
PMID: 28198672
DOI: 10.1186/s12864-016-3254-5

[…] in which co-occurrence correlation networks based on different periodontitis states were inferred and compared.fig. 1 , tagcleaner [22] was used to remove sequencing tags. tags were predicted by tagcleaner with coverage over 50%. read sequences at either end representing tags without mismatches were removed. prinseq [23] was then used to remove low-quality reads. those reads with mean […]

2017
PMCID: 5310281
PMID: 28198672
DOI: 10.1186/s12864-016-3254-5

[…] the disease “status” can be stable, progressing or unknown., we constructed a bioinformatics pipeline (fig. 1) consisting of six steps, as follows: (1) quality control and preprocessing, in which tagcleaner, prinseq, deconseq and flash [22–25] were used to remove low quality reads and contamination from the human genome; (2) expanded phylogenetic analysis, in which metaphlan [26] was used […]

TagCleaner institution(s)
Department of Computer Science, San Diego State University, San Diego, CA, USA; Computational Science Research Center, San Diego State University, San Diego, CA, USA; Department of Biology, San Diego State University, San Diego, CA, USA; Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
TagCleaner funding source(s)
Supported by grant DBI 0850356 Advances in Bioinformatics from the National Science Foundation.

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