dChip statistics

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Citations per year

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

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

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Popular tool citations

chevron_left Gene expression clustering Background correction Bioinformatics workflows Normalization Batch effect correction Bioinformatics workflows chevron_right
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Protocols

dChip specifications

Information


Unique identifier OMICS_00719
Name dChip
Alternative name DNA-Chip Analyzer
Software type Pipeline/Workflow
Interface Graphical user interface
Restrictions to use None
Operating system Windows
Computer skills Medium
Stability Stable
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Cheng Li

Additional information


https://sites.google.com/site/dchipsoft/tutorials-and-courses

Publication for DNA-Chip Analyzer

dChip citations

 (8)
library_books

Blood Transcriptional Signatures for Disease Progression in a Rat Model of Osteoarthritis

2017
Int J Genomics
PMCID: 5512050
DOI: 10.1155/2017/1746426

[…] ained signal was taken as the measure of mRNA abundance derived from the level of gene expression. Microarray data were standardized using a z-score calculation. Statistical analysis was performed by dChip software using ANOVA filtering and followed by correction for multiple testing (estimation of expected false positives). Hierarchical clustering was performed using the measure of Euclidian dist […]

library_books

Trans effects of chromosome aneuploidies on DNA methylation patterns in human Down syndrome and mouse models

2015
Genome Biol
PMCID: 4659173
PMID: 26607552
DOI: 10.1186/s13059-015-0827-6

[…] ng our statistical threshold by from 15–20 % in the several comparisons. To assess DNA copy number we used the intensity values from the 450K arrays, after normalization and model-based expression in dChip [].We further analyzed 12 DNA samples on the 450K BeadChips using an adaptation of the Illumina probe preparation protocol, in which the use of a modified bisulfite conversion procedure (BS/OXBS […]

call_split

Adaptive evolution and divergent expression of heat stress transcription factors in grasses

2014
BMC Evol Biol
PMCID: 4094458
PMID: 24974883
DOI: 10.1186/1471-2148-14-147
call_split See protocol

[…] ice tissues), GSE6901 (expression data for heat, cold and salt treatments), and GSE14275 (expression data for heat-shock conditions) were used in an expression analysis of rice Hsf genes. The program dChip version 2010 [] was used to perform the cluster analysis and to display the expression patterns of rice Hsf genes based on the microarray data. Gene-expression values were compared using the pro […]

library_books

Efficient and Comprehensive Representation of Uniqueness for Next Generation Sequencing by Minimum Unique Length Analyses

2013
PLoS One
PMCID: 3548888
PMID: 23349747
DOI: 10.1371/journal.pone.0053822

[…] nloaded from UCSC Genome Browser on July 3rd 2011. The CpG island annotations were downloaded from UCSC Genome Browser mm9 annotation track (30 August 2011). Cytoband annotations were downloaded from dChip website (http://sites.google.com/site/dchipsoft/home) on September 6th 2011. […]

library_books

Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort

2012
BMC Genomics
PMCID: 3464625
PMID: 22702538
DOI: 10.1186/1471-2164-13-241

[…] o make use of the ratio of allelic intensities which can improve CNV prediction []. Until very recently [], little has been done for Affymetrix 500 K arrays, which were analyzed with software such as dChip [], CNAG [], GEMCA [] and CNAT []. All but CNAT are restricted to the Windows operating system and thus are inappropriate for the analysis of large cohorts and for distributed computing on UNIX- […]

library_books

NEUROD6 EXPRESSION DEFINES NOVEL RETINAL AMACRINE CELL SUBTYPES AND REGULATES THEIR FATE

2011
Nat Neurosci
PMCID: 3144989
PMID: 21743471
DOI: 10.1038/nn.2859

[…] tes were profiled per transgenic line, each representing a sample of ~1000 cells collected from a single litter. Data was analyzed and cell-type-specific genes identified using Resolver (Rosetta) and dChip software. Heatmaps were constructed by first filtering all probesets to remove those that did not show at least one 3-fold change between any pair of cell types, then standardizing the expressio […]


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dChip institution(s)
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
dChip funding source(s)
Supported by NIH grant 1R01GM077122

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