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Using native and syntenically mapped cDNA alignments to improve de novo gene finding.

Stanke, M Diekhans, M Baertsch, R David Haussler

Published in Bioinformatics

Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Ev...

Paull, Eo Carlin, De Niepel, M Sorger, Pk David Haussler Stuart, Jm

Published in Bioinformatics

The UCSC Known Genes.

Hsu, F Kent, Wj Clawson, H Kuhn, Rm Diekhans, M David Haussler

Published in Bioinformatics

The University of California Santa Cruz (UCSC) Known Genes dataset is constructed by a fully automated process, based on protein data from Swiss-Prot/TrEMBL (UniProt) and the associated mRNA data from Genbank. The detailed steps of this process are described. Extensive cross-references from this dataset to other genomic and proteomic data were cons...

Into the heart of darkness: large-scale clustering of human non-coding DNA.

Bejerano, G David Haussler Blanchette, M

Published in Bioinformatics

Efficient approximations for learning phylogenetic HMM models from data.

Jojic, V Jojic, N Meek, C Geiger, D Siepel, A David Haussler Heckerman, D

Published in Bioinformatics

Support vector machine classification and validation of cancer tissue samples using microarray expression data.

Furey, Ts Cristianini, N Duffy, N Bednarski, Dw Schummer, M David Haussler

Published in Bioinformatics

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Vaske, Cj Benz, Sc Sanborn, Jz Earl, D Szeto, C Zhu, J David Haussler Stuart, Jm

Published in Bioinformatics

PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis.

Ng, S Collisson, Ea Sokolov, A Goldstein, T Gonzalez-Perez, A Lopez-Bigas, N Benz, C David Haussler Stuart, Jm

Published in Bioinformatics

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources.

Diekhans, M Kelly, L Thomas, Dj Pieper, U Eswar, N David Haussler Sali, A Karchin, R

Published in Bioinformatics

The variant call format and VCFtools.

Danecek, Petr Auton, Adam Abecasis, Goncalo Albers, Cornelis A Banks, Eric DePristo, Mark A Handsaker, Robert E Lunter, Gerton Marth, Gabor T Sherry, Stephen T ...

Published in Bioinformatics (Oxford, England)

The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was develop...

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