BioHMM statistics

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Citations per year

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

This map represents all the scientific publications referring to BioHMM per scientific context
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BioHMM specifications

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Unique identifier OMICS_08930
Name BioHMM
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Stability Stable
Maintained No

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No version available

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This tool is not available anymore.

Publication for BioHMM

BioHMM citations

 (15)
library_books

Combined Targeted DNA Sequencing in Non Small Cell Lung Cancer (NSCLC) Using UNCseq and NGScopy, and RNA Sequencing Using UNCqeR for the Detection of Genetic Aberrations in NSCLC

2015
PLoS One
PMCID: 4468211
PMID: 26076459
DOI: 10.1371/journal.pone.0129280

[…] s to zero across the entire genome. Direct visualization was used to assess structural variations across the genome. Finally, segmentation was performed by a heterogeneous hidden Markov model, termed BioHMM [], which was adapted for NGS data.To calculate gene-level CNV in the 07–0120 tumor tissue cohort, we used the depth of gene exon-specific sequenced reads with 1-bp resolution. We estimated the […]

library_books

Autoregressive Higher Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles

2014
PLoS One
PMCID: 4067306
PMID: 24955771
DOI: 10.1371/journal.pone.0100295

[…] xMod and DSHMM, which both were specifically developed for the analysis of tumor expression profiles . Additionally, we also include methods from the closely related field of aCGH analysis (Wavelet , BioHMM , FHMM , CBS , CGHseg , GLAD ) into this comparison. A more detailed summary of considered methods is given in Table S2 in .We applied all methods with their proposed initial standard settings […]

library_books

biomvRhsmm: Genomic Segmentation with Hidden Semi Markov Model

2014
Biomed Res Int
PMCID: 4065698
PMID: 24995333
DOI: 10.1155/2014/910390

[…] nal information or clone quality in the modeling process and, thus, could be considered as an extension of the HMM in package aCGH. Among these models, there has been no comparison study between bcp, bioHMM, and HaarSeg in recent literature. We did not include implementations that are specific to SNP data in our comparison, mainly due to the unique nature of the platform, which is less general in […]

library_books

A High Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data

2012
Adv Bioinformatics
PMCID: 3449101
PMID: 23008709
DOI: 10.1155/2012/876976

[…] MRIs filtered by the amplitude-dependent prioritization method (i.e., those in Supplementary Table S1) and resulted in the reordering shown in Supplementary Table S2.Using this modified GTS approach, BioHMM and GLAD again led to identification of SEC61G as the top gained locus, while HomHMM led to SEC61G being placed second, behind SKAP2. DNAcopy did not lead to identification of SEC61G gain, inst […]

library_books

A Multi Sample Based Method for Identifying Common CNVs in Normal Human Genomic Structure Using High Resolution aCGH Data

2011
PLoS One
PMCID: 3205051
PMID: 22073121
DOI: 10.1371/journal.pone.0026975

[…] We compared MGVD with the seven algorithms implemented in CGHWeb , namely CBS , FASeg , cghFLasso , CGHseg , Quantreg , GLAD , and BioHMM . We ran these algorithms on chromosome 22 only, as this is the smallest chromosome, because all seven algorithms terminated after several days when run on data from all chromosomes. For each a […]

library_books

An initial comparative map of copy number variations in the goat (Capra hircus) genome

2010
BMC Genomics
PMCID: 3011854
PMID: 21083884
DOI: 10.1186/1471-2164-11-639

[…] from the averaged ~6 kb. Pointwise averaging of all computed profiles and maps of gains/losses for smoothed/segmented obtained from several algorithms (Lowess, Wavelet, Quantreg, ruavg, CBS, CGHseg, BioHMM, cghFLasso, GLAD, and FASeg) and summary data were generated. Pointwise averaging was shown to have good performances in calling alteration of copy number [] and was chosen to compensate possib […]


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BioHMM institution(s)
Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge; Department of Applied Mathematics and Theoretical Physics, University of Cambridge Wilberforce Road, Cambridge

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