After years fighting, our Sclsut paper published on Nature Protocols finally. Enjoy!
Yupeng Cun, Tsun-Po Yang, Viktor Achter*, Ulrich Lang, Martin Peifer, Copy number analysis and inference of subclonal populations in cancer genomes using Sclust. Nature Protocols, 2018，DOI: 10.1038/nprot.2018.033
Frequent Q&A on Sclust software package uses：
Our new methods final launched on Nature Protocols, where we developed a series of methods and related C++/R combined software package, Sclust（around 1.5Gb,大文件谨慎载）. In Sclust, you can do copy number calling, cancer tissue purity estimating and clone and subclone structure inferring from normal-tumor paired whole genome/exon sequencing data.
1. 可以准确地做copy number calling， tumor purity estimating，subclonal inferring；
2. subclonal inferring的速度超级快。4000~6000 个SNVs 的 clonal inferring 过程在个人电脑上只需3到5秒。
3. sclust 给出了每个集群的倍数树变异，目前还有少数个软件提供这个功能。
联系邮件：firstname.lastname@example.org。 下面clonal 推断一些背景。
A new R package, netClass, has been release. netClass integrate network information, such as protein-protein interaction network or KEGG, to mRNA classification, but also incorporate miRNA to mRNA with mi-mRNA interaction network for biomarker discovery. This methods we called stSVM and already published in PloS ONE (Cun et al 2013). Apart from stSVM, we also implement the flowing methods in netClass:
- AEP (average gene expression of pathway), Guo et al., BMC Bioinformatics 2005, 6:58.
- PAC (pathway activitive classification), Lee E, et al., PLoS Comput Biol 4(11): e1000217.
- hubc (Hub nodes classification), Taylor et al.(2009) Nat. Biotech.: doi: 10.1038/nbt.152
- frSVM (filter via top ranked genes), Cun et al. arXiv:1212.3214 ; Winter etal., PLoS Comput Biol 8(5): e1002511.
- stSVM (network smoothed t-statistic) , Cun et al., PloS One,.
NetClass can be download from souceforg ( http://sourceforge.net/projects/netclassr/) or , CRAN (http://cran.r-project.org/web/packages/netClass/ ). For more detail of netClass, you can refer these four papers:
A review collection in current approach in Translational Bioinformatics.
‘Translational Bioinformatics’ is a collection of PLOS Computational Biology Education articles which reads as a “book” to be used as a reference or tutorial for a graduate level introductory course on the science of translational bioinformatics.
Translational bioinformatics is an emerging field that addresses the current challenges of integrating increasingly voluminous amounts of molecular and clinical data. Its aim is to provide a better understanding of the molecular basis of disease, which in turn will inform clinical practice and ultimately improve human health.
The concept of a translational bioinformatics introductory book was originally conceived in 2009 by Jake Chen and Maricel Kann. Each chapter was crafted by leading experts who provide a solid introduction to the topics covered, complete with training exercises and answers. The rapid evolution of this field is expected to lead to updates and new chapters that will be incorporated into this collection.
Collection editors: Maricel Kann, Guest Editor, and Fran Lewitter, PLOS Computational Biology Education Editor.
Download the full Translational Bioinformatics collection here: PDF
Collection URL: www.ploscollections.org/translationalbioinformatics
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002796
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002826
Casey S. Greene, Olga G. Troyanskaya
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002816
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002805
Mileidy W. Gonzalez, Maricel G. Kann
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002819
Dong-Yeon Cho, Yoo-Ah Kim, Teresa M. Przytycka
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002820
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002821
Konrad J. Karczewski, Roxana Daneshjou, Russ B. Altman
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002817
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002858
Nigam H. Shah, Tyler Cole, Mark A. Musen
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002827
Xiang Zhang, Shunping Huang, Zhaojun Zhang, Wei Wang
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002828
William S. Bush, Jason H. Moore
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002822
Xochitl C. Morgan, Curtis Huttenhower
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002808
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002823
Miguel Vazquez, Victor de la Torre, Alfonso Valencia
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002824
In our recent publication in BMC bioinformatics, we acompared a great deal of feature selection methods to finding prognostic biomakers in 6 breast cancer gene expresion data. No methods show significant performacne in prediction accuracy, feature selection stability and biogical interprety, which against previeous reseach results: current network-based appraoch did not show much benift in our analysis. Meanwhile, A group from NKI also show the simliar results in PloS One. The R codes for these algorithms in our paper is availiable as request.
In 2003, after more than a decade of research, the Human Genome Project was completed by the U.S. Department of Energy and the National Institutes of Health.
The goals of the Human Genome Project were to learn the order of the 3 billion units of DNA that go into making a human genome, as well as to identify all of the genes located in this vast amount of data. By 2003, almost all of the pairs of chemicals that make up the units had been put in the correct sequence—enough for a pronouncement of success. The individual genes within the long strands of DNA, and the elements that control the genes, are still in the process of being identified. Current counts indicate that the human genome contains 22,000 to 23,000 genes.
One of the early hopes of the genomic project was to pinpoint specific genes that caused common diseases. Scientists now think the answer is more complex, with many diseases the result of multiple genes interacting. Nevertheless, the information garnered from the genome project has the potential to forever transform healthcare. Many believe that genome-based medicine, frequently called personalized medicine, is the future of healthcare—the next logical step in a world in which more is known about human genetics, disease, and wellness than ever before.
Of all the scientific and social promises that stem from advances in our understanding of the human genome, genomic medicine may be the most eagerly awaited. The prospect of examining a person’s entire genome, or at least a large portion of it, in order to make individualized risk predictions and treatment decisions is tantalizingly within reach.